In an interview at the recent AI & Big Data Expo, Streambased founder and CEO Tom Scott outlined the company’s approach to enabling advanced analytics on streaming data. At the foundation of Streambased’s offering is Apache Kafka, an open-source event streaming platform that has been widely adopted by Fortune 500 companies.

“Where [Kafka] falls down is in large-scale analytics,” explained Scott. While Kafka reliably transports high-volume data streams between applications and microservices, conducting complex analytical workloads directly on streaming data has historically been challenging. 

Streambased adds a proprietary acceleration technology layer on top of Kafka that makes the platform suitable for the type of demanding analytics use cases data scientists and other analysts want to perform.

Because these continuously flowing event streams power critical operational systems and core business functions, data quality must already meet high standards in terms of accuracy, timeliness, and structure. By leveraging these existing Kafka data pipelines, Streambased ensures its analytical capabilities have access to up-to-date, clean and well-organised data.

Use cases that showcase the power of Streambased’s approach include fraud detection in financial services. If an anomalous transaction occurs, analysts can quickly query similar or related transactions to investigate – which would be difficult and inefficient to accomplish with a pure streaming architecture. Streambased’s optimization for analytical interactivity enables users to rapidly gather contextual insights without disrupting their workflow.

The convergence of operational and analytical data platforms represents an impactful trend that Streambased calls the “streaming data lake” movement

“I think we are at the period of the streaming data lake movement. And by a streaming data lake, I mean a complete convergence between data systems that we use for analytical purposes and data systems that we use for operational purposes,” explains Scott.

Recent enhancements like infinite data retention in Kafka and native streaming analytics services lay the foundation for this new paradigm. For now, Streambased remains focused on empowering business analysts through frictionless self-service access to granular real-time data, without requiring changes to existing tools and processes.

You can watch our full interview with Tom Scott below:

(Photo by Robert Zunikoff on Unsplash)

See also: AI & Big Data Expo: Unlocking the potential of AI on edge devices

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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On a mission to bridge the gap between the worlds of business and analytics, Infocepts was founded in 2004 by me and Rohit Bhayana, both with more than 20 years of experience in the Data and Analytics (D&A) industry. People often use the term business analytics as one phrase, but if you have worked in the industry for a long time and if you talk to a lot of people, you’ll realise just how big the gap is.

And that’s Infocepts’ focus. We are an end-end D&A solutions provider with an increasing focus on AI and our solutions combine our processes, expertise, proprietary technologies all packaged together to deliver predictable outcomes to our clients. We work for marquee enterprise clients across industries. Infocepts has the highest overall ranking on Gartner peer reviews amongst our competitors and we are a Great Place to Work certified firm. So, we’re very proud that our clients and our people love us.

The data & analytics technology market is evolving very fast. What’s your view of it?

I love being in the data industry and a large reason is the pace at which it moves. In less than 10 years we have gone from about 60-70 technologies to 1,400+ and growing. But the problems have not grown 20X. That means, we now have multiple ways to solve the same problem.

Similarly, on the buyer side, we have seen a huge change in the buyer persona. Today, I don’t know of any business leader who is not a data leader. Today’s business leaders were born in the digital era and are super comfortable not just with insights but with the lowest level data. They know the modeling methods and have an intuitive sense of where AI can help. Most executives in today’s world also have a deeper understanding about what data quality means, its importance, and how it will change the game in the long run.

So, we are seeing a big change both on the supply and demand side.

What are some of the key challenges you see in front of business & data leaders focused on data-driven transformation?

The gap between the worlds of business and analytics is a very, very real one. I would like to quote this leadership survey which highlights this contradiction. Talking about D&A initiatives which are adding value – 90% of data leaders believe their company’s data products provide real business value, but only 39% of business leaders feel so. That’s a huge gap. Ten years ago, the number would have been lower, but the gap was still the same. This is not a technology issue. What it tells us is that the most common roadblocks to the success of D&A initiatives are all human-related challenges like skills shortages, lack of business engagement, difficulty accepting change and poor data literacy throughout the organisation

We all know the power of data and we spoke about business leaders being data leaders, but there are still people in organisations who need to change. Data leaders are still not speaking the language of business and are under intense pressure to demonstrate the value of D&A initiatives to business executives.

The pace at which technologies have changed and evolved is the pace at which you will see businesses evolving due to human-centric changes. The next five years look very transformational and positive for the industry.

Can you also talk about some of the opportunities you see in front of the D&A leaders?

The first big opportunity is to improve productivity to counter the economic uncertainty. Companies are facing financial crunch because of on-going economic uncertainty including the very real possibility of a recession in the next 12-18 months. Data shows that there are companies that come out strong after a recession, with twice the industry averages in revenue & profits. These are the companies who are proactive in preparing & executing against multiple scenarios backed by insights. They redeploy their resources towards the highest value activities in their strategy and manage other costs. Companies need to stop paying for legacy technologies and fix their broken managed services model. To keep up with the changing technology landscape, it’s important to choose on-demand talent. 

Secondly, companies and people should innovate with data and become data fluent. Many organisations have invested in specialised teams for delivering data. But the real value from data comes only when your employees use it. Data fluency is an organisational capability that enables every employee to understand, access, and apply data as fluently as one can speak in their language. With more fluent people in an organisation, productivity increases, turnover reduces, and innovation thrives without relying only on specialised teams. Companies should assess their organisational fluency and consider establishing a data concierge. It’s like a ten layered structure instead of a very big team. A concierge which can help you become more fluent and introduce interventions across the board to strengthen access, democratise data, increase trust adoption.

Lastly, there’s a huge opportunity to reimagine how we bring value to the business using data. Salesforce and Amazon pioneered commercially successful IaaS, PaaS, and SaaS models in cloud computing that gradually shifted significant portions of responsibility for bringing value from the client to the service provider. The benefits of agility, elasticity, economies of scale, and reach are well known. Similarly, data & analytics technologies need to go through a similar paradigm and go one step further towards productised services, what we call at Infocepts – Solutions as a Service!

Can you talk more about your Solutions as a Service approach?

What we mean by Solutions as a Service is a combination of products, problem solving & expertise together in one easy to use solution. This approach is inevitable given the sheer pace at which technology is evolving. This new category requires a shift in thinking and will give you a source of advantage like how the early cloud adopters received during the last decade. Infocepts offers many domain-driven as-a-service solutions in this category such as e360 for people analytics, AutoMate for business automations and DiscoverYai (also known as AI-as-a-Service) for realising the benefits of AI.

There is a lot of buzz around AI. What does AI mean for the world of D&A and how real is the power of AI?

Oh! It’s very real. In the traditional BI paradigm, business users struggled to get access to their data, but even if they crossed that hurdle, they still needed to know what questions to ask. AI can be an accelerator and educator by helping business folks know what to look for in their data in the first place.

AI-driven insights can help uncover the “why” in your data. For example, augmented analytics can help you discover why sales are increasing and why market penetration varies from city to city, guiding you towards hidden insights for which you didn’t know where to look.

Another example is the use of chatbots or NLP driven generative AI solutions that can understand and translate queries such as, “What are sales for each category and region?” Thanks to modern computing and programming techniques combined with the power of AI, these solutions can run thousands of analyses on billions of rows in seconds, use auto ML capabilities to identify best fit models & produce insights to answer such business questions.

Then, through natural language generation, the system can present the AI-driven insights to the user in an intuitive fashion – including results to questions that the user might not have thought to ask. With user feedback and machine learning, the AI can become more intelligent about which insights are most useful

In addition to insights generation, AI can also play a role in data management & engineering by automating data discovery, data classification, metadata enhancements, data lifecycle governance, data anonymisation and more.

On the data infra side, models trained in machine learning can be used to solve classification, prediction, and control problems to automate activities & add or augment capabilities such as – predictive capacity & availability monitoring, intelligent alert escalation & routing, anomaly detection, ChatOps, root cause identification and more. 

Where can AI create immediate impact for businesses? Can you share some examples?

AI is an enabler for data and analytics as against being a technology vertical by itself. As an example, let’s look at the retail industry – use cases like store activity monitoring, order management, fraud/threat detection, assortment management have existed for a while now. With AI, you can deliver them way faster.

In media, some of the use cases that we are helping our clients with are around demand prediction, content personalisation, content recommendation, synthetic content generation – both text & multimedia. AI also has vast applications in banking. We again have fraud detection, and coupled with automation, now it’s not just detection but you can also put controls in real time to stop fraud.

We have also implemented AI use-cases within Infocepts. We leverage AI to increase our productivity & employee engagement. Our HR team launched ‘Amber’, an AI bot that redefines employee experience. We use AI assistants to record, transcribe and track actions from voice conversations. Our marketing & comms teams use generative AI tools for content generation.

The advancement we have seen in the tech space in the last few years is what you will see in the next 3 to 4 years on the people side. And I think AI assisted tech processes and solutions will play a huge role there.

What advice would you give business leaders who are looking to get started with AI?                             

Embrace an AI-first mindset! Instead of the traditional approach of tackling complex business problems by sifting through data and wrestling with analysis for months before you see any results, it’s important to embrace an AI-first mindset to get things done in no time! AI-driven auto-analysis uncovers hidden patterns and trends so analysts can get to “why” faster and help their business users take actions. The auto-analysis gives data teams access to hidden patterns and the dark corners of their data. Let your AI tools do most of the grunt work faster than your traditional approaches. And now with Generative AI technologies bolted on top of these solutions, you can make it conversational using voice or natural language search capabilities.

Solutions like Infocepts DiscoverYai does just this. It gives organisations the opportunity to make smart choices based on data-driven insights. Our process starts by identifying clients’ objectives and then leveraging advanced AI strategies that quickly assess data quality, highlight key relationships in your data, identify drivers impacting your results and surface useful recommendations as well as provide an impact analysis resulting in actionable recommendations that have maximum impact potential – all delivered through an effective combination of tried-and-tested practices along with cutting edge AI driven processes!

Secondly, to gain the most from AI-driven insights, you’ll need to be ready for a little experimentation. Embrace getting it wrong and use those discoveries as learning opportunities. Hackathons/innovation contests are a great way to generate quick ideas, fail fast and succeed faster.

It’s also essential that your team can confidently understand data; this enables them to recognise useful actions generated by artificial intelligence without hesitation. So, while you use AI, ensure that it is explainable.

Lastly, help your organisation set up systems which will make sure your AI models don’t become obsolete in an ever-evolving landscape – keep upping their training so they remain ready to take on even harder challenges!

About Shashank Garg

Shashank Garg is the co-founder and CEO of Infocepts, a global leader in the Data & AI solutions. As a trusted advisor to CXOs of several Fortune 500 companies, Shashank has helped leaders across the globe to disrupt and transform their businesses using the power of Data Analytics and AI. Learn more about him on LinkedIn.

About Infocepts

Infocepts enables improved business results through more effective use of data, AI & user-friendly analytics. Infocepts partners with its clients to resolve the most common & complex challenges standing in their way of using data to strengthen business decisions. To learn more, visit: infocepts.com or follow Infocepts on LinkedIn.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Infocepts CEO Shashank Garg on the D&A market shifts and impact of AI on data analytics appeared first on AI News.

As data sets increase and become more complicated, manual analytics processes become less feasible. And as data gathering evolves, analytics has to keep up. That’s where advanced analytics joins the party. With the use of machine learning, data mining, and advanced modeling techniques, advanced analytics can turn an organisation’s vast amount of data into increasingly accurate predictions. 

Even more, advanced analytics can help identify trends and predict the best steps to yield positive outcomes. This helps organisations forge a path of strong and sustainable growth.

Adopting advanced analytics: a story of resistance and confusion

Despite the tremendous value-add advanced analytics can provide, it’s still often met with confusion and resistance in organisations. For example, in the UK, recent research commissioned by Exasol, the analytics database, found that 63% of UK data decision-makers experience resistance from employees in adopting data-driven methods. They attribute this resistance to anxiety over job redundancy, a lack of understanding, and a lack of education on the positive impact of data analytics. 

The report further reveals that part of the problem of data acceptance lies in 40% of respondents admitting that data strategy is not being driven by anyone in the organisation. With advanced analytics becoming mission-critical to all businesses, organisations must implement a clear data-driven strategy and ensure buy-in from all employees and stockholders.

Fundamental misunderstandings sit at the heart of the user-adoption dilemma. Business Intelligence (BI) developers have built powerful tools, but they weren’t necessarily appropriate for the mindset and skillset of the end-user. They relied too much on the user being an analyst at heart.

The idea then became that if the tools were comparatively easy to use, all knowledge workers could turn into analysts. But that wasn’t necessarily the case either. Analytics has to be translated into action. And BI tools have to work for the end-user in order for the tools to become an integrated and natural part of staff workflow.

Infused, advanced analytics: a powerful step towards a data-first culture

Infused analytics (also called embedded analytics) is vital to making data abundantly available in a format that suits the needs of the user. It puts actionable intelligence from analysed data into every workflow, process, business application, and even internally developed products. 

This is the power of Sisense. With the Sisense platform, users enjoy fully customised experiences, driven by APIs that deliver the right information, at the right time and place, and in a way that makes sense to the user. This speeds up time to action, simplifies decision-making, and increases productivity. Because infused analytics sits within the existing technology stack a team already uses, they don’t have to learn a new tool or switch between platforms in order to gain insights. 

For organisations that desire to create a data-first culture, it’s imperative to take the user experience seriously. A global study from Exasol, the analytics database, found that 65% of data teams have experienced employee resistance to adopting data-driven methods. The two main reasons for the resistance was a lack of understanding of the organisation’s data strategy and a lack of education about the positive impact data brings. This confusion is having a detrimental effect, curbing data culture transformation as a whole. 

Organisations who are starting their engagement with analytics need to make the data consumable and “bite-sized”. They must enable the process, so there is a limited gap between insight to action. In other words, take the relevant data to the person contextualised and in the workflow they want to use.

To the business world, this is relatively new. But modern-day already sees people, albeit rather unknowingly, gleaning insights from data in their daily lives in ways that benefit them. Smartwatches, for example, leverage data to let people know when it’s time to stand up and walk around to meet personal step goals. 

Popular apps and products have made the process of extracting value from data completely seamless, and in many cases, invisible. The data is so easy to consume because it’s right there and in the right context when it’s needed.

The takeaway? Users will adopt analytics when it works for them. That is, when it’s easy to access, non-intrusive, clear, direct, and provides the relevant insights that connect to their needs.

Advanced but accessible BI: engagement on their terms

Despite the numerous benefits of using data-driven insights for business decision-making,  the simple fact of the matter is that not everyone is interested in becoming a data analyst. This is especially true today in the post-pandemic business landscape where many teams are already understaffed and feeling burnt out. 

Knowledge workers would love to take action and make more decisions based on data, but not at the expense of their time. So continuous training to learn additional skills outside of their jobs just isn’t a solution.

To that end, BI tools must be user-friendly. And for a tool to be considered user-friendly, it has to shield the end-user from the complexities of the data. For example, business people think in business terminology. They think of opportunities, customers, pipelines, and revenue. So that’s how they need to be able to interact with data – on their terms and in a way that makes sense to them.

They don’t need to concern themselves with the complexities that underpin it all. Instead, they need the flexibility to ask questions in a natural way as opposed to a technical way. An advanced analytics tool like Sisense that provides the power to build custom-reporting dashboards offers the best of both worlds.

These dashboards can be tailored to the business needs and fit the questions that a variety of users may have. The visual dashboards also provide an at-a-glance summary of the data, which is perfect for keeping data easily digestible.

Making advanced analytics accessible brings an organisation one step closer to solving the user-adoption dilemma. And it helps create an engaged culture where end-users experience the value of custom dashboards for decision making. Dashboards can show users what they want to see, when they want to see it, and in a format that’s easy to understand.

Put simply, advanced analytics is one of the most powerful tools for business. There’s a treasure trove of information available to any company that’s willing to unlock it. That’s information that can provide a clear path to growth and profitability.

To dive deeper into advanced analytics and its benefit to business, download the How to Boost Your Business with Advanced Analytics ebook by Forecast here.

The post Ready to boost your business with analytics? This data expert reveals all appeared first on AI News.

The problem, given the sheer volume and variety of that data, is where to look first. So much of the data available to law enforcement data analysts and senior staff is unstructured. In other words, it doesn’t line up in an orderly fashion in a relational database or spreadsheet. Police forces collect data of many different types – images from CCTV, phone records, social media conversations and images, and so on. Tying that variety of sources together to achieve valuable insights is difficult.

It demands the very latest in data integration tools, able to aggregate all information of possible relevance and present it so that it delivers insights via a single, easy-to-use platform and allows correlations between datasets to be discovered. With today’s data visualisation techniques, a picture emerges from different data sets without time being wasted on wading through information. Organised criminals work fast and change tactics regularly. Time lost in elaborate and complex manual data searches can give them the chance they need to move on and evade detection.

Data visualisation is critical to today’s law enforcement efforts. It complements data analytics, converting information collected from various sources into a clear picture, displayed using familiar elements such as graphs, charts, and maps. By using natural language processing as well as artificial intelligence and machine learning capabilities, otherwise invisible patterns emerge.

An easily digestible view of data can help in several ways. Here are a few of them:

Interpreting visual data: The human brain can process visual data 60,000 times faster than it does text. Data visualisation gives law enforcement professionals a crucial edge because smart visual tools amplify human abilities and allow them to more easily spot anomalies or patterns in the data. They can also better understand operations, identify areas for improvement, and uncover missing evidence links for faster case resolution.

Deploying predictive analytics: Having access to predictive and prescriptive analytics means that law enforcement professionals can build and deploy statistical models that provide alerts when new incidents are likely to happen, with context on circumstances that require pro-active investigation. Data visualisation is core to this because it provides an easy-to-understand translation of machine learning models and presents actionable intelligence. Patterns can be spotted, giving law enforcers a critical head start. Simple visual techniques such as assigning a range of amber to red colours to areas of concern on a map are highly effective.

Sharing critical data: Data visualisation is not just of academic use to data scientists. It is useful for everyone in the law enforcement team, from officers on the street to supervisors and analysts in the office. Detectives investigating organised crime can use the visual output of these tools to see the connections between people, property and financial transactions within a crime syndicate without needing data science qualifications. Anyone can see what the data is saying. Different teams, indeed different police forces, can share information seamlessly without fear of system incompatibilities.

More than that, today’s tools can aggregate all the relevant information within and outside an agency and analyse it to deliver insights via a single platform. Crucially here, data can be handled in a secure manner so only those with the appropriate clearance can see it.

Managing tight resources: Law enforcers are always looking for more efficient resource allocation and better ways to juggle limited amounts of personnel and equipment. Badly organised resources can impact everything from crime clearance, departmental morale, and perception in the community. With a data visualisation platform, they can spot areas that need immediate and long-term attention. They can also see which crimes have the biggest community impact and therefore need the most resources.

Improving community relations: Data visualisation gives police a chance to connect with their communities, demonstrating the results of their work in a digestible and interactive form. They can showcase incident-rate trends, initiate awareness about emerging security concerns and foster community engagement. Sharing data builds trust and cooperation, making it easier in the longer term to gather evidence and solve cases.

The right platforms are available today to allow law enforcers to make faster and more accurate decisions. The insights derived from visual analytics are already helping keep law enforcement personnel and civilians safe, reduce operational costs and improve investigation outcomes.

The police are not in a position to share all of the successes they have enjoyed with data visualisation, but others can. For example, how the Scottish Environment Protection Agency (SEPA) uses data to address the threat of illegal polluters offers a close and relevant comparison.

SEPA has a vital role in working with government, industry and the public to ensure regulatory compliance with environmental rules. It has a range of enforcement powers which it can apply to ensure that regulations are complied with. However, enforcement relies on the ability to intelligently analyse data from multiple sources, on air, water and soil quality for example.

SEPA has millions of records dating back decades in a huge variety of formats and used to rely on manual collection, analysis and reporting of its testing samples to set alongside historic data to help spot pollution trends. With an analytics platform supplemented by data science and visualisation, SEPA has built a range of customisable solutions to address a wide variety of data-related tasks. Staff members carry visual analytics on a tablet wherever they go. No longer needing to write code or carry physical binders of data analyses, they can run data analytics on the spot and answer questions in the moment. Use cases can involve looking at pollutants, ecology and lab measurements, while others have covered industry compliance, laws and licences.

Just as it has done for SEPA, data visualisation can help law enforcers to identify never-before-seen patterns in data to make better decisions now and help steer future direction to resolve hidden challenges in their effort to reduce crime.

(Photo by Scott Rodgerson on Unsplash)

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However, the report shows there are some challenges to overcome. For example, 53% of respondents wish their analytics experience was more aligned with user-friendly entertainment applications, such as Netflix and Spotify.

Analytics drive business value

92% of product decision-makers say that data and analytics are critical to the success of their businesses. More than 4 in 5 (86%) say that offering data and analytics to their customers plays a critical role in not only the satisfaction of those customers but also in terms of building and retaining loyal customers.  

And for a direct tie to the bottom line, 96% note that an increase in average selling prices would be possible with personalized and customized analytics, with 46% noting they could charge 10-19% more for their products and services because of the analytics they provide.

Benefits of embedded, actionable personalised intelligence

Nearly all decision-makers (94%) feel companies that they are able to deliver data and analytics at the right time to the right people are considered innovative.

Other key points:

• 96% believe their customers are interested in having AI-driven insights that can provide actionable, personalised intelligence in the context of their activity
• 97% think their customers are interested in analytics provided in the context of the task the user is completing
• 97% note that customers want analytics more personalised to the specific end user
• 96% feel customers want data customised to their industries or consumer activity
• 95% think their customers want interactive analytics
• 56% believe that customers would find prescriptive analytics most useful 

Looking to the future, 81% of product decision-makers say that if they could provide their customers with personalised data and analytics, it should be provided by embedding those into communication software or platforms, custom-built apps or off-the-shelf business or SaaS applications.

Current barriers to success with analytics

While the numbers above speak to the opportunity, 83% of decision-makers think their customers currently are making decisions without proper data and analytics at least sometimes. 

However, product decision-makers cite barriers in being able to deliver such offerings. 41% of decision-makers cite legal and compliance requirements as an issue. 38% say their customers have difficulty accessing information. And this access may in large part be due to the fact that 92% of decision-makers deliver data and analytics to customers via non-embedded methods such as email and dashboards, requiring them to disrupt their workflows to go elsewhere for critical information.

Predicting what’s next for analytics in 2022

“The results from this third-party study are directly in line with what we are hearing from our customers and see in 2022 for analytics. Firstly, we expect organizations will redefine what it means to build a ‘culture of analytics’ by bringing insights to workers in a more digestible way, such as embedding them into regular processes so no new skills are required. Secondly, most data-driven organisations will combat tool fatigue by bringing data to workers where they are, directly within their workflows,” said Sisense Chief Product and Marketing Officer, Ashley Kramer. 

“And lastly, we see automation turning descriptive analytics, that demonstrate what already happened, and predictive analytics, profiling what will happen, into prescriptive guidance, focusing on what the best course of action to take is to make smart, proactive decisions,” Kramer continued.

Editor’s note: This article is in association with Sisense

(Photo by Hunters Race on Unsplash)

The post 92% of product decision-makers say data and analytics is critical to success appeared first on AI News.

The solution, Scene Analytics, will initially be deployed for Baselland Transport AG (BLT) in Münchenstein, Switzerland.

Michael Theiler, Head of Maintenance Electrical Systems at BLT, said:

“Level crossings are notoriously difficult areas to ensure the safety of passengers, pedestrians, train operators and motorists.

This deployment, in collaboration with Nokia, represents an encouraging step towards using analytics as another layer of protection in dangerous areas.

Nokia Scene Analytics acts as an intelligent set of ‘eyes’ and, by providing critical information in real-time, to prevent or mitigate the impact of an incident.”

Scene Analytics combines machine learning and computer vision capabilities to enable real-time analysis of video feeds. The solution can be trained on CCTV data to learn what is “normal” or anomalous.

Karsten Oberle, Head of Rail at Nokia, commented:

“As the first deployment of its kind in Europe, this project with Schweizer Electronics and BLT enabled us to address many of the level crossing safety issues which are at the top of priority lists for rail operators.

It is now our ambition for Nokia Scene Analytics to become a key part of the transition towards the digitalisation of future railways.

By integrating machine learning into level crossing systems, we will be able to continuously improve and refine safety processes in real-time. This will ensure that safety remains at the forefront for train operators, workers, and customers alike.”

Scene Analytics can be integrated with many standard cameras; reducing the cost and time required to buy and install new hardware across the rail network.

The solution also helps to reduce costs – while improving the rail service for passengers – by minimising downtime and delays.

In a report, the EU identified (PDF) around 250 fatalities and 300 serious injuries relating to rail crossings in 2018 across its then-28 member states.

Here were the estimated costs of railway accidents in the EU in 2018 alone:

• Fatalities – €2.89 billion
• Material damage, Costs of delays, Costs to the environment – €393 million
• Serious injuries – €379 million
• Other costs – €142 million

By deploying AI-powered video analytics for railway crossings, operators can hopefully lower costs and even save a few lives.

(Photo by Piotr Guzik on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Nokia deploys AI video analytics to improve rail crossing safety appeared first on AI News.

Executives also report that the adoption of analytics is at a standstill, and they have yet to realise the long-term benefits of their investments. According to NewVantage Partners, the biggest hindrance to increasing the adoption of analytics is the inability to build a data-driven culture throughout the organisation. Just a quarter of companies report that they achieved their goals of forging a data culture and creating a data-driven organisation. 

But despite these trends, leading executives are choosing to invest more than ever before on business intelligence to better adapt to the evolving competitive landscape. In fact, Gartner reports that even during a pandemic, most executives accelerated investments in data analytics.

What can be done to bridge the gap and drive true data-driven leadership?

In short, company leadership is failing to lead by example. They do not embrace data and analytics, and in turn, their teams have failed to adopt data-led approaches to their work. The end result is the entire business choosing to neglect intelligence that could provide beneficial strategic insights, opportunities to drive growth, increase revenue, and speed past the competition.

Redefining being data-driven 

For too long, we have assumed that being data-driven solely means using technology, often in the form of charts, dashboards or visualisation tools. We invested millions in enrolling employees in analytics classes, data science certificates, and teaching people how to query a database. As the NewVantage Partners report shows, this clearly hasn’t worked. 

Instead, their report suggests, in relation to achieving a data culture, “a slightly increased percentage from last year — now 92% — attribute the ‘principal challenge to becoming data-driven’ to people, business processes, and culture, with only 8% identifying technology limitations as the barrier.”

What the future of being data-driven looks like

As the leaders of their teams, executives must develop a new vision around what it means to be an intelligence-led company. They should advocate for an organisational culture that adopts analytics as more than just a best practice. They must also redefine what it means to be data-driven.

Being data-driven goes deeper than “bring charts to meetings” or “make decisions with numbers.” It means implementing a hypothesis-driven culture where we identify theories, test them, and rigorously seek to disprove them while rapidly implementing those that show promise. We must make decisions based on evidence. Don’t let your teams search out favorable statistics. Encourage them to look at the complete data picture and come to conclusions based on the preponderance of the evidence.

Lower barriers to entry to drive data adoption

The key to increasing the adoption of analytics is lowering the barrier to entry, bringing the right insights to the point of decision rather than expecting individuals to seek out new data. Traditionally, we asked users to dig through dashboards, submit data requests, or wait for reports to make informed decisions. This was a starting point as many organisations began their quests to become data-driven, but it was also error-prone, distracting, and quite simply, slow. 

We must adopt new ways of delivering analytics insights if we want users to test different ideas and make quick decisions based on data at every turn. To do this, we have to bring the data to the user within their business tools rather than creating yet another destination for decision-making. Instead of asking workers to leave their CRM, their productivity tools, the apps they use to make decisions and take action, we need to bring the data to them, seamlessly embedding it in the tools they are already using.

Today, this is the single most important factor to evaluate in BI solutions. According to the Sisense-commissioned IDC Internal Analytics Survey 2020, 61% of business leaders say incorporating analytics into their existing workflows is one of their biggest objectives while choosing third party solutions.

Time to take ‘ownership’ of data leadership

Finally, executives must take the lead to drive analytics adoption and become the organisation’s chief evangelists. They must make use of a multidisciplinary toolbox of skills—including experiential, creative, and analytical, to gain insights to shape data-driven business strategy. The days of passing off data questions to specialists and analysis are over, and if we are going to ask everyone in the organisation to be data-driven, that change must start from the top.

Take CMOs, for example, who must combine creative thinking with a surge in customer and prospect data to target the correct audience with the right marketing channels. Today’s buyers prefer to learn about products on their own, rather than engage with salespeople right from the start. This puts the onus on CMOs to make smart choices about advertising, content, and demand generation, and they must make these decisions in hours and days, not weeks and months.

Another example are sales leaders. Today’s CSOs and CROs must do more than set quarterly goals before retreating to the sidelines as cheerleaders. They now have the data to investigate forecasted revenue shortfalls, measure the pace of sales reps, test various sales cycle lengths, and more. Implementing a metric and hypothesis-driven culture ensures the team abandons sales tactics that don’t work and rapidly implements the ones that do. In the end, the whole company predictably hits its revenue targets. Everybody wins.

The top down approach to data-driven success

To succeed in today’s quickly-changing business environment, the entire company must make the shift to becoming more analytical, and it is up to all executives to lead the way. 

In a McKinsey survey of C-level execs and senior managers, responders from high-performing organisations were 70% more likely to have a data leader in the C-suite. Their organisations were also more likely to foster a culture of data-driven iteration, rapid testing and failure, and making data accessible to everyone in the company, even frontline employees.

This means the onus is on executives to champion data initiatives, infuse analytics into business workflows so everyone can use actionable intelligence, and they must be examples of a data-driven culture to the rest of the company.

Editor’s note: This article is in association with Sisense

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According to the 2020 UK Business Data Survey, 81% of all businesses surveyed say they handle digitised personal data, digitised non-personal data, or both, and use of data increases considerably as businesses become larger. 

This means organisations need to bring in the right talent to make sure their data is being used appropriately and effectively. But what does it take as a data engineer to keep up with the innovation needs of today’s fast-paced businesses? And are we expecting too much from today’s analytics talent?

‘The solvers of all business problems’

Despite widespread recognition of the value of data in business, budgetary constraints, skills challenges, education around data, and the best use of employees’ time, remain key challenges. There is also the risk of data teams being seen as ‘the solvers of all business problems’ – and they become overloaded with irrelevant questions.

In fact, what we’re seeing more than ever are the data teams are suddenly becoming the ‘perceived source of all business decisions’, while the CEO, the CTO, the CFO are just ‘hammering them’ with questions and requests. 

​​What this is doing is creating a bottleneck issue whereby engineers are becoming bombarded with requests that could often be solved by analysts or self-serve BI tools. This means their resources are taken up on things that aren’t the most efficient use of their time or driving real value for the business.

Great data engineers are highly coveted and hard to come by, so companies have to start thinking smarter about their data strategy and how to prioritise requests for their engineers in order to ensure companies are making the most use of their teams and talent.

Understanding the role data plays in business success is essential for making the most of the talent and skills a data engineer has within a business. Tools and technology are a crucial part of that success, but the value of it will only be realised when enabled by the right people and process.

The need for data engineers to drive better business decisions

Decisions need to be driven by the actual real-life data, rather than intuition or passive observation, says Carly Metcalfe, Director of Data Engineering at Forth Point. 

“This will lead to more accurate, more optimal decisions being made on the ground,” Carly explains. “Data-driven decision makers should have a deep understanding of what purpose data serves, why the problems we seek to solve exist in the first place and how data can ultimately be used to improve the lives of people around the world.”

By focusing on the real-world problems that data can solve and providing tangible benefits from decisions arising from this data, the UK’s image as a leader in data-driven engineering and leadership will be boosted, Carly adds. 

Organisations can foster a data-driven culture through:

• Having a transparent data strategy in place; 
• Buy in from the top of the organisation;
• Data Governance measures; and
• Data assets data lineage being documented and made available.

Critical skills of a modern data engineer

According to Carly, to be a good data engineer, you need three key things: communication, problem-solving skills and agility.

For communication, she explains, “Understanding the challenges facing a client and communicating this information to the rest of the team are vital skills required to ensure that projects run smoothly. 

“A great data engineer will be able to interpret complex requirements, analyse possible solutions and suggest the benefits and potential downsides to any approach.”

For problem solving, Carly says the majority of a data engineer’s time can be spent optimising or problems solving issues with data rather than building the pipeline.

When it comes to agility Carly highlights that building data pipelines can be a challenging and complex task. 

“Compatibility issues may arise if the structure of the data provided ends up being different to that which was agreed upon,” she adds. “A client may also prefer a specific tool or methodology. An agile data engineer can pivot and adjust the approach as required to suit the requirements.”

Understanding the tools 

According to Carly, the amount of tools and applications in the workplace right now can make it overwhelming for data engineers to do their job. 

“The sheer scale of tools and applications that are out there can make it challenging at times to get to grips with all the latest technology and concepts,” she says. “The volume of data that needs to be processed can also cause challenges of its own.”

Currently in Scotland, particularly Edinburgh, Forth Point has recognised there is a shortage of data engineers, notes Carly. 

“I would say we need to have more data engineering degrees and graduate schemes. We are fortunate to have universities such as Edinburgh and Napier which offer data engineering degrees, she says. “But we need more courses that can teach the foundational skills required to be a data engineer.”

The evolution of the modern data team

The future looks exciting. Companies that are moving beyond traditional BI are those that are disrupting markets. They’ve invested in modern data teams to optimise insights across the organisation, driving growth and ROI in the process.

Organisations that have a data driven mindset are encouraging frequent discussion and offering plenty of opportunity for learning, embracing failure and successes and keeping a strong focus on data security.

Where data teams of the past may have had three traditional participants: database administrators responsible for data warehousing capabilities and capacity, data analysts focused on data modelling, and BI architects in charge of building dashboards and related self-service reporting capabilities, today the team is so much more.

Typically a team that partners with the business but has its own specialised skills, this group does everything from outlining business rules to modelling data to building single sources of truth.

The role of a great engineer in this kind of team is to be the strategic thinker – what are the patterns that are emerging from the adhoc requests?  How do we build the sustainable assets that will allow us to scale our delivery to the business without growing costs?  How can we make life easier for us and for our downstream users?  

The answers to these questions may include advanced techniques like applying machine learning or some statistical and predictive technologies to determine patterns or gaps where your organisation simply does not have ideal data.  It’s about blending the strategic and theoretical, with the tactical and practical.

A great data engineer will also increasingly be blending model data with raw data from multiple sources. Ideally, your data team can take raw data coming in from a new system and join it with existing model data, such as account, opportunity, or service data, to answer questions that may not have been anticipated in a current data model.

If data is blended from multiple sources into a single warehouse, does that process occur before or after it arrives at the warehouse? For example, a more modern approach would blend the data post warehousing. Data engineering includes elements of problem solving and solution design that have traditionally been associated with architectural roles.

Unlike a traditional BI team that provides only a data model, today’s data team operates as a core function of the business and recommends a strategy built on data. In a modern data team engineers, analysts and BI developers are working cohesively in an organisation to provide up to date and precise information. The group has a seat at the table, offering informed opinions — based on data — about what the company should be doing.

Editor’s note: This article is in association with Sisense

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Players, pioneers, and leaders

For most of modern history, coaches had little more than pen, paper, and video to help them analyse play. During the ’90s, Manchester United became one of the pioneering teams to adopt analytics in their decision-making; by 2010, analytics were becoming widely adopted by teams in leading international leagues.

In recent years, football experienced rapid technological advances, with platforms able to capture and analyse data from training, match play, internet of things (IoT) devices, and wearables. Coaches now rely heavily on metrics to guide their decision-making and help their teams excel. 

Infusing analytics to enhance play

Football teams rely on huge amounts of data drawn from countless sources to take their play to the next level. IoT sensors and GPS devices track player and ball movements in real time. Optical tracking can even pinpoint the position of players on the pitch 25 times a second, in relation to the ball, opposition, and teammates.

In training, wearable devices measure players’ workload, movement, and fatigue levels to manage their fitness and positioning and optimise performance during play. The data collected by these devices are also used to design personalised training plans. During matches, coaches can also see in real time how each player is performing to help guide strategic substitutions. 

This is infused analytics at work: Wearable devices deliver data and insights directly to the coaches, enabling them to make decisions and transform teams’ performance without technical data expertise.

Modern data storage on the cloud gives teams the ability to collect and mash up vast volumes of data from these devices. Big data analytics and artificial intelligence enable the simultaneous processing and analysis of data from many sources to measure and even predict performance. These developments have added new dimensions to data analysis and to football!

The same trend has happened in business. Technology offers leading companies more ways to generate data than ever before. As top companies began to benefit from the analytics they had adopted, increasing numbers of other organisations followed their lead. Analytics have proven to be a winning formula, on and off the pitch.

How teams benefit from analytics

The most valuable intelligence for coaches shows what happens in real time. This means coaches can use this data to change the shape of their teams and behaviour to increase their likelihood of winning. These types of insights are mainly gathered from playing logs, video, and GPS tracking, and spatially related data.

Heat map data visualisations have shown that teams which keep possession of the ball and maintain high intensity are most likely to score goals and win games. 

Team data can also be analysed as a network, in which nodes represent players and the lines between the nodes represent interactions, such as passes between teammates. Coaches can identify different types of interactions and encode different types of events. This data allows them to identify, change, and test the effectiveness of typical passages of play. 

This can be seen in a study of passing networks in football which analysed a Barcelona v Real Madrid match in 2018. Another recent analysis confirms the leading teams in Europe’s top five leagues (Bundesliga, La Liga, Ligue 1, Premier League, Serie A) achieve their success with more passes per match, particularly in the Bundesliga, the Premier League, and Serie A.

By analysing data, football teams can identify the winning ways to play and, using the same techniques for their team analysis, they can determine which teams do it best and how.

Real-time data and predictive analytics

Predictive analytics take analysis into the future and can be used to help coaches understand the consequences of altered team formations and other changes. Coaches can tailor training, strategy, and individuals’ roles according to data about their next opponents. Business managers can use data in the same way to tailor their approaches to different customers, staffing levels, inventory, and more.

A metric that teams have discovered has predictive power is expected goals (xG), which measures the quality of players’ shots in attacking play and the probability the shots will result in goals. xG uses algorithms that account for factors such as distance from goal, angles, and more. 

Coaches use this metric to try to predict the best positioning of players and patterns of play to optimise scoring opportunities. In this case, analytics provide insights into the most effective strategies to apply in different situations.

With that in mind, data can be put to work to help recruit the right team members for future success or fast-track others.

Teams use a wealth of data about players’ strengths and weaknesses to gain a deeper insight into individual performance to help them and the club succeed. What’s required to harness the power of all this data is an analytics platform that can handle huge and growing sets of data points from a multitude of live and cached sources, then visualise it all in ways that can provide fast, comprehensible, and actionable insights.

Data makes winners

Analytics in football is opening incredible opportunities, especially as IoT, wearables, and more sophisticated platforms become normalised across sporting tournaments, clubs, and associations. Football teams’ infusion of analytics into team management, training, performance analysis, and recruitment has become an essential part of their success. And as more clubs look to data for insights into how they can build winning teams, the future is exciting for both players and fans.

Editor’s note: This article is in association with Sisense.

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As vaccines continue to roll out, a hybrid work model has emerged, with staff now alternating and ‘taking turns’ being back in the office. In the US, research done by SHRM.org highlights that 55% of the workforce favours a hybrid workforce post-pandemic. 

In the UK, a survey by PWC found 77% of UK employees want a mix of face-to-face and remote working. As a result, 77% of UK organisations plan to reconfigure their existing office space and 50% think they will reduce the size of their office portfolio. 

However, there is one key concern from companies in the face of all this change; How does one maintain security in a hybrid workforce?

The answer involves putting the focus on maintaining company culture through education and communication, infusing data analytics to understand human error and how to mitigate it, and engaging in security planning and testing. 

The importance of educating staff

The emergency conditions that necessitated a widespread move to working from home in 2020 were far from typical. Many workers had trouble acclimating, stress levels were high, and distractions were plentiful.

Due to these obstacles, 55% of IT professionals said reducing human error and training employees on how to properly operate security systems from home was their biggest challenge in 2020. Educating employees on the importance of cybersecurity and giving them open access to continued training needs to start early.

For example, developing an internal trust centre for all things security gives employees a central location to find answers and resources easily. It also provides direct links to the company’s documentation on security protocols, settings, systems, and operations.

One organisation helping to scale education through data-driven insights in the workforce amidst the pandemic is Learning Pool, which has embedded Sisense BI tools to combine the power of its leading AI-driven analytics cloud platform with the world’s most-widely installed Learning Record Store (LRS), Learning Locker. 

With more than a billion data points under management, Learning Pool customers use Learning Locker to help manage their learning records.

Learning Pool is getting ready to launch a new addition to the Learning Pool suite, aptly named “Insights”. This new addition will incorporate Sisense’s AI capabilities for personalisation and automatic intelligence, helping transform data into real insights.

Insights will create expertly designed dashboards powered by AI, built specifically for learning data analysis. 

By presenting accessible, adaptable and actionable visualisations that are grouped together as meaningful dashboards, Insights provides Learning Pool clients with actionable insights about the past, present and potential future performance of their learning ecosystem and their users.

Communication in a hybrid environment

One downside to hybrid or remote environments is the lack of in-person interactions. Having to connect virtually may not come close to real-life engagements. However, considering many people prefer working remotely (65%, according to a PWC survey) and others are open to hybrid models (55%, according to Flexjobs.com), connecting virtually is key for maintaining company communication and fostering culture. 

As an example, when COVID caused Sisense to become a full-fledged work-from-home entity, setting up communication tools like Slack; creating virtual events, gatherings,  town hall meetings, and subscribing to video conferencing tools like Zoom, created simple pathways for teams and leadership to stay connected. Contrary to many business leaders’ concern, virtual communications will impact company culture but in many ways, the change improved culture. 

This connection and increased communication boosted the culture of Sisense, and that directly impacted security. People feel connected to the company, and as a result, they have something they care about protecting.

During COVID, one initiative Sisense had to maintain as part of their work culture was to offer employees self care days. 

“This is a day off each month dedicated for employees to take time to themselves, recharge, be with their families or sleep in– whatever they needed for that day was up to them,” Amir Orad, CEO at Sisense said.

“The key was (and still is) that the self care day is mandatory, we insist everyone take the day off together and make it legitimate to be away from zoom and email. The feedback we received from employees was that they felt supported and seen during a difficult time.”

Making workplace safety a priority with AI

When the pandemic hit and the world was forced to retreat into seclusion, some companies were more prepared to pivot effectively than others. Those who put safety and security by leveraging data and insights to do so, set themselves up to pull through stronger than ever before.

As an example, Air Canada used the Sisense Fusion Platform, was able to build powerful proofs of concept to improve safety across the airlines.

Air Canada was able to go ‘beyond the dashboard’ with new innovations to deliver insights to its frontline workers with the use of the Sisense Fusion Platform and pushing actionable data to their stakeholders (frontline employees and executives). In this way, Air Canada enabled these stakeholders to focus and immediately act upon important insights about their day-to-day activities, especially activities relating to safety. This helped add insights about safety across the organisation and improve safety measures for the end-user. 

The Canadian airline developed these innovative improvements as a result of their ability to deliver crucial and personalised data to their employees and frontline workers. 

Testing led to innovations in internal communications for Air Canada. The airline started sending real-time notifications to employees’ smartwatches and using the Amazon Echo speakers as a communication tool for staff to ask questions immediately in their own words. Additionally, Air Canada was able to transform traditional dashboards into immersive 3D environments which used mixed reality to display data in different ways. 

Results proved that there was not a one-size-fits-all approach in sharing data and insights with employees. It was clear that not all employees digested data the same way. However, as a result of the innovations, staff will now receive the same data differently to personalise what each employee wants and needs from the data. 

Future-proof your workforce with AI-infused analytics

As COVID rampaged, claiming lives, flipping the global economy and uprooting the habits of millions; Ty Sbano, Sisense Chief Security Officer’s team implemented their pandemic plan quickly, they learned as they went, and worked out kinks as they arose. 

“We adjusted our plan to fit exactly what was happening,” he said. “Having a contingency plan on paper for if and when an event hits puts our team ahead of the curve.” 

The planning phase for the security team included defining critical business workflows, creating a secure system for employees to use, and strengthening security protocols (like Zscaler) to enable the entire global company to continue working without interruption. The security and IT teams at Sisense worked diligently to test plans before they’re executed, uncover any gaps, software issues, or better routes for security success. 

While error will never be completely eradicated, it can be managed with the right AI-infused data-driven and innovative measures. Your data will be protected, your teams will be connected, your workforce feel more safe, and your business culture will have the opportunity to grow in a new, and potentially stronger, direction.

Editor’s note: This article is in association with Sisense.

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