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2016 Trends in Data Analytics: More Hands Take Hold of the Power

By   /  January 26, 2016  /  No Comments

2016_trends_analytics_01251Data Analytics is expanding its reach. In its report, Software 2016: Analytics, Acceleration and Agility, 451 Research predicts that analytics will become more prevalent throughout the layers of technology businesses use, from development to IT management, and databases to customer experience management.

This is happening as the natural result of the increasing digitization of business, which throws off massive amounts of new data. Along with that, new tools, techniques, and architectures provide the opportunity to analyze what could not previously be analyzed, and the chance exists for companies to differentiate themselves based on how they take advantage of that opportunity, the report says.

“We are going to look back in eight to 10 years and any business decision not driven by analytics is going to look absurd,” says 451 Research Vice President of Software, Nick Patience.

Of course, there has been a broad understanding and usage of Business Intelligence and Analytics tools for years, he acknowledges, and there will continue to be a pressing need for expert Data Scientists who understand algorithm design, Machine Learning, and building business models. But, a significant change taking place that will make analytics use more widespread among general business and IT staff, software developers and others is that businesses needn’t make huge IT investments to provide access to analytics technology to more users. Today, they can leverage solutions such as Cloud-based analytics, Patience says.

“Analytics is being done on the predictive and prescriptive side that couldn’t be done a few years ago partly because computing infrastructure has changed and Cloud Computing is where it is,” he says. Additionally, the tools themselves are becoming more powerful even as they are becoming easier to use. “That is a fairly important catalyst for change,” he says. Self-service online analytics products, often with free trials included, proliferate in the Cloud for anyone to try out, and the prescriptive functionality available today means that users can rely on those capabilities to help them optimize what they learn about predicted outcomes.

“In the past you might have gotten all your data nicely collated, but then didn’t know what to ask of it or how to do it,” Patience says. Now, users can even posit their thoughts in natural language to drive analytics efforts. “We are at that stage with some tools where they can understand what you ask and translate that into a query and give you results,” he says.

Others agree that tools increasingly are there to be put into the hands of business users to enable them to gain and act on insights that can deliver a competitive edge. “The Big Data eco-system is evolving and maturing, with a rich set of tools and capabilities which make Big Data Analytics simpler and more intuitive than ever before,” says Ajay Anand, Vice President of Products at Big Data Analytics solutions vendor Kyvos Insights. It itself is one such vendor, providing scalable OLAP on Hadoop technology that lets users quickly visualize, explore, and analyze Big Data interactively, working directly on Hadoop.

Digital Transformation Steps Forward

Data Analytics provide a way to help companies harness digital transformations that are affecting almost every sector. Patience points to some big names that are projecting their understanding of the need to embrace business digitization and propel it with analytics, and Data Scientist and development talent, in big ways. Companies like GE and its Industrial Internet efforts come to mind, as does John Deere, which Patience says is putting electronic sensors into tractors to proactively understand maintenance issues before they happen, “that kind of change for them is fairly profound,” he notes.

Anand sees data growing across the board in all kinds of industries it deals with – financial services (including credit card providers), risk analysis, insurance, media and entertainment, cable and telecommunications companies, and defense and intelligence. “The business imperative to get insights from data to better understand customers and gain a competitive edge exists with almost every customer we talk to,” he says. “And, of course, with technologies such as IoT, the spread of data being collected will grow even larger.”

Data Analytics will surge further ahead in the form of contextual analytics, according to 451 Research – that is, the combination of text and advanced analytics with Machine Learning to uncover insight from a combination of structured and unstructured data. Applying advanced analytics with Machine Learning has been an industry goal for the last decade, Patience says. Adoption will kick into higher gear now that algorithms are better at understanding unstructured data like text, so that intelligence can be derived from it in context with existing structured data.

Industries poised to benefit include healthcare, where doctors’ textual notes can be automatically analyzed in real time and in context with structured data on medical records. Machine Learning algorithms can be used to help find patterns in that data that could point to patient problems before they occur, or to determine if patients can be treated at home vs. the hospital, leveraging not just structured medical data but doctors’ unstructured text notes about factors such as whether they live alone.

“Analytics with Machine Learning gets extremely powerful,” Patience says. “The software learns part of the business problem to an extent, and it can spot for patterns if you are looking for them or for outliers.”

Anand agrees that Machine Learning is becoming increasingly prevalent in the way businesses are leveraging data, noting that recommendation engines, predictive modeling, market-basket analysis, and customer segment analysis are some common examples. “The value is highly enhanced if you can run these algorithms directly on granular data, instead of having to move it to another database and run statistical tools on samples or subsets of the data,” he says. Anand says that Kyvos customers connect directly from packages like R to its solution to get results in seconds so that they can iterate on the Machine Learning algorithms that they would like to apply.

Assessing the Value of Growing Data Analytics in Digital Business

Anand says there’s vast opportunity for companies in a digital world to do more with analytics to improve operational efficiency as well as revenue producing initiatives. But, as much as there is opportunity in changing the way businesses think about data, so too is there a challenge, he says, including in dropping the constraints that the systems they often have relied on have imposed due to limitations of memory, space or computing power. “This opens things up to innovative ways to get insights,” he says.

For example, companies that use Kyvos’ solution can consolidate data into the Hadoop infrastructure to eliminate data silos and reduce the number of steps needed to get insights, he says. “By making analytics available directly on Hadoop, they can reduce the overall processing time from weeks to hours.” And when it comes to revenue growth, it is important to make the data accessible and easy to navigate for the business users who are working on market opportunities, he states. According to Anand, “with tools such as Kyvos, business users can explore, analyze, and examine different aspects of the data directly, regardless of size or complexity, to derive the insights they need to make business decisions.”

Patience notes that in any organization there will be some individuals or departments that understand the ongoing digital transformation less clearly than others and who are less willing than others to invest in analytics to get the most out of it. But it won’t be that way forever.

“Data Analytics will be here, there and everywhere and we will want it to be that way, for a deep understanding of customers or even employees,” he says.

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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