The top data trends for 2017 revisited

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Many organizations make predictions for the year ahead, but too few of those firms follow-up with a look at how well they did in anticipating trends.

Now Infogix, a data and analytics software provider has done just that, revisiting its predictions for the top trends in data management and analytics for 2017. Now that we’re more than halfway through 2017, Infogix looked back to see which trends were hits, which were misses and which needed an update according to their analysts.

“Infogix’s 2017 trends examined the evolving ways enterprises can unlock business value with big data, remove barriers to adoption, and steer business direction using advanced analytics predictions to transform organization processes and the customer experience,” explains Sumit Nijhawan, CEO and president of Infogix. “The predominant theme weaving through the key trends of 2017 is data quality; if you do not know the quality of your data, then organizations experience a triple play compromise – lowered big data adoption rates, doubting value of predictive insights, and flawed decision making.”

The top 10 data trends for 2017 were originally assembled by a panel of Infogix experts at the end of 2016. Here is a look at how these trends have progressed throughout the year.

Trend One: The Proliferation of Big Data…

“Many organizations have turned to data lakes based on the power of three business benefits – lower storage costs, ability to store unstructured, semi-structured, and structured data, and potential to unlock predictive insights for competitive advantage,” the firm noted. “However, most organizations have treated their big data environment as data dumping grounds, with limited to no quality control, and forgotten that some semblance of data governance and quality control is necessary.

“The one false belief that must be smashed is that an organization should store/hoard as much data as possible with hopes of doing analytics down the road. What we have seen so far is organizations that have taken this approach have extremely low utilization of big data due to general distrust in using data that has no quality control metrics.”

…Update to Trend One

“Moving forward, organizations need to implement data governance around their big data environments,” the firm says. “This will allow them to answer three strategic questions –
1. What’s the definition of the data?
2. Who owns the data?
3. What is the quality of the data?

“Understanding data from a business context and confirming its quality will improve business adoption and unlock more future insights from your big data.”

Trend Two: The Use of Big Data to Improve the Customer Experience…

“Many organizations have now updated their core systems and have moved from legacy to vendor systems,” the firm explains. “They are using big data to better understand customers and comply with government regulations to eliminate any fines and increase revenue.”

…Update to Trend Two

“Today, organizations want to use their data to get a full 360-degree view of their customers to deliver the best experience possible,” the firm explains. “The push is on for organizations to apply machine learning, artificial intelligence, and advanced analytics to get this 360-degree view and predict customer behavior.”

Trend Three: Wider Adoption of Hadoop…

“Virtually every organization has a big data environment,” the firm states. “Organizations are using these environments to crunch substantial amounts of data using advanced analytics and machine learning to find nuggets of valuable information for making profitable decisions.”

…Update to Trend Three

“Organizations are starting to set up smaller environments within their data lakes so they can keep certain data separate from other data,” the firm says. “In addition, many organizations are now looking for data quality and data governance tools to help manage their data lakes.”

Trend Four: Hello to Predictive Analytics…

“Organizations have been predicting future customer behaviors and events for increased profitability for some time now,” the firm says. “They have also used predictive analytics to improve fraud detection to minimize revenue risk exposure and improve operational excellence.”

…Update to Trend Four

“Looking ahead, organizations want to predict the behavior of their data, where it travels, what processes it goes through, how accurate is it, and is it on time to help determine the quality of their data,” the firm explains. “With proper data governance and self-service analytics, this is possible.”

Trend Five: More Focus on Cloud-based Data Analytics…

“Organizations have moved data analytics to the cloud to accelerate adoption of the latest capabilities to turn data into action,” the firm says.

…Update to Trend Five

“The cloud is now the norm in virtually every industry,” the firm says. “For the rest of 2017 and beyond, it’s about meeting hosting requirements and standards for cloud vendors – everything from handling the location of data to security.”

Trend Six: The Move toward Infomatics and the Ability to Identify the Value of Data…

"Organizations are trying to use infomatics to help integrate the collection, analysis and visualization of complex data to derive revenue,” the firm explains.

…Update to Trend Six

“Now that everyone understands the importance of monetizing their data, they now want to quantify it, making infomatics very popular. But while everyone is striving to do infomatics, the verdict is still out on the best methodology to do it properly,” the firm notes.

Trend Seven: Achieving Maximum Business Intelligence with Data Virtualization…

“Graphical data virtualization allows organizations to retrieve and manipulate data on the fly regardless of how the data is formatted or where it is located,” the firm says.

…Update to Trend Seven

“Organizations are trying to optimize infrastructure so they can make all their data sources more dynamic,” the firm says. “The evolution that most organizations are experiencing is that users are demanding more personalization to massage data into ways that they use it to solve specific problems rather than live with static one-size fits all dashboard views. It is a cost-effective way to move and store data.”

Trend Eight: The Convergence of IoT, Cloud, Big Data and Cybersecurity …

As we continue to become more reliant on smart devices, interconnectivity and machine learning will become even more important to protect these assets from cyber security threats,” the firm predicts.

…Update to Trend Eight

“Organizations are now struggling to ensure the privacy of their customer’s data and are turning to a combination of three technologies – machine learning analytics, data governance and data quality,” the firm says. “They are looking for an integrated platform to protect data. This trend is gathering momentum for organizations that operate within Europe and need to comply with the General Data Protection Regulation (GDPR), which encompasses personal data privacy spanning IoT, cloud, big data, and cybersecurity.”

Trend Nine: Improving Digital Channel Optimization and the Omnichannel Experience…

“Delivering the balance of traditional channels with digital channels to connect with the customer in their preferred channel is huge in today’s customer-driven world,” the firm says.

…Update to Trend Nine

“Real-time information is important to customers across web and mobile platforms, but without instituting data quality checks and balances there is a risk that data being delivered to customers contains errors and omissions,” the firm says. “This poses a reputational, compliance, and business liability that must be addressed with a cross platform solution to maintain data quality regardless of data source.”

Trend Ten: Self-Service Data Preparation and Analytics to Improve Efficiency…

“Self-service data preparation tools boost time to value enabling organizations to prepare data regardless of the data type,” the firm says.

…Update to Trend Ten

“IT teams are still frustrated because of the many inquiries from business teams and the increase in multiple cases to the original request,” the firm notes. “This back and forth exchange can be alleviated with a self-service toolset which enables business teams to take the data IT has prepared and easily edit and modify it. When the name of the game is to move at the speed of business, enabling the business with self-service data preparation tools is a game changer to accelerate the end goal of answering business questions faster and more efficiently.

“We’ve seen the shift to big data environments to take advantage of the information that data has to offer. But the struggle remains in the data’s trust. If you can’t confirm your data’s quality, you can’t trust it to make the right business decisions. Data quality has turned to big data quality and only those organizations that realize it’s threat and potential will come out ahead,” says Nijhawan.

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