Making Big Data Analytics Accessible To Non-Data Scientists

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Making Big Data Analytics Accessible To Non-Data Scientists

The key to a successful Big Data strategy is having a clear goal and purpose from the outset. Impactful data helps companies can define goals across the organization.

· Big Data Zone ·
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I recently had the opportunity to talk to Guy Levy-Yurista, Head of Product at Sisense, regarding the state of Big Data today. Sisense is at the forefront of making big data insights available to all employees across the enterprise.

How Is Your Company Involved in Big Data?

Sisense is a business intelligence solution that simplifies business analytics for complex data by offering an end-to-end platform that lets users easily connect to, prepare, and analyze large, disparate data sets. Sisense handles the full BI cycle from data preparation to analysis and dashboard visualization. Sisense is also on the forefront of transformation in the BI market, leveraging AI, machine learning and IoT technologies to humanize business analytics and bring data to life in the real world.

What Are the Keys to a Successful Big Data Strategy?

The key to a successful Big Data strategy is having a clear goal and purpose from the outset. Companies today collect and store an unimaginable amount of data, but what makes that data impactful is when companies are able to use insights extracted from that data to inform business decisions and ultimately drive success.

Additionally, companies need the right tools in place that can help to democratize those insights across an organization. Data is no good if it is locked in IT for days or weeks on end. By making data more accessible to all employees, companies can further define goals across an organization and identify the right data to support those efforts.

How Has Big Data Changed in the Past Year?

Big Data technology is now available everywhere and is easily accessible to everyday users. The rise of self-service solutions has enabled companies to embrace data in new ways and truly implement a data-driven strategy. In fact, Gartner pointed to this shift earlier in 2016 in its Magic Quadrant for BI and Analytics, removing six companies from the ranking because their solutions required heavy IT involvement. They replaced these companies with solutions that put the power of analytics directly into the hands of business users.

At Sisense, we have gone a step further to humanize data insights by turning to AI and IoT technologies that bring data to life in employees’ natural work environments. These innovations are part of our Sisense Everywhere program and include connecting your data with the Amazon Echo, an IoT-enabled lightbulb, and the introduction of Sisense BI Bots — a chatbots platform that lets users have a two-way conversation with their data using the messaging app of their choice (such as Slack or Skype).

What Technical Solutions Do You Use to Collect and Analyze Data?

There is no silver bullet when it comes to collecting and analyzing data. It is best for companies to evaluate first what the business challenge they are trying to solve, and then embark on evaluating specific solutions. The important part is to make sure that you choose a technology that can wrangle complex data — large and disparate data sets from multiple sources — as this is increasingly what companies today are facing.

What Real World Problems You or Your Clients Are Solving With Data?

One of the best parts of our solution is that we see new applications from our clients happening every day. We have more than 3,500 named organizations we support from SMBs to Fortune 500 companies, across verticals such as financial services, retail, manufacturing, healthcare, and so on.

In one instance, connecting to the previously discussed IoT integration, we have a customer using endpoint data connected to an IoT-enabled lightbulb. The light changes in correspondence to key KPIs, driving an emotional response for employees. When the lightbulb is green, that means that the KPI is being met or exceeded. The light then changes to yellow or red if the KPIs drop below certain predetermined levels.

One such KPI was around engagement with a platform being introduced to a customer’s environment. That company saw the adoption of this platform jump from 16 percent to 85 percent following the implementation of the Sisense-enabled SmartLight IoT lightbulb because workers want to avoid seeing a red light.

This exemplifies the commonality we see from our customers when it comes to solving data problems: they are always searching for ways to take Big Data and drive meaningful response from the average employee or consumer.

Additionally, we have started to see companies look for creative ways to use data to introduce new revenue streams. Across industries, companies today are dealing with a growing influx of complex data, and one of the biggest corporate money wasters is having to store all this data in warehouses. As companies navigate the evolving digital landscape, data monetization serves as a valuable avenue to allow companies to take advantage of wasted data to adapt their business and stay a step ahead of the competition.

What Prevents Companies From Realizing the Benefits of Big Data?

I see two issues come up that most often inhibit companies from realizing the full potential of their data. First is the inability to dynamically connect different data sources; data is useless if it only exists in silos and cannot be connected to provide a holistic picture. The second issue, which is related, is the need for constant human interaction or manual processing to make these connections. Data insights need to operate with some level of automation so that humans can focus on higher level activities and use the data to drive action.

Where Are the Biggest Opportunities in the Evolution of Big Data?

As the Big Data landscape continues to evolve, the further dissemination of data will be critical. Companies need to break down the notion that Big Data is only for the technically advanced or executive team, and bring Big Data, business intelligence, and analytics to 100 percent of its workforce. In the digital age, organizations will only become more data-driven and data fluency should be as accessible and as common nature as reading and writing.

What Skills Do Developers Need to Work on Big Data Projects?

The Big Data landscape is constantly changing. We see new technologies and innovations pop up all the time. For developers, they can never expect to have a full grasp of every technology, but I would say a top quality should be that they are excited about the future and the rapid development of technology — not threatened by it.

Our global development centers embrace the challenge of staying ahead of the curve and are constantly adapting and learning when new developments arise.

I would also recommend developers engross themselves with AI and Machine Learning, as those technologies continue to be a growing focal point for the industry.

big data, big data strategy, data analytics, data science

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