There is no doubt about it. Millennials, unlike previous generations, love self-service options. They want the freedom to customize orders, change account information, and find answers to their questions without intervention from a middle man. Think of them as the first demographic who prefers navigating automated phone systems over speaking to a real person, at least most of the time.
This is even true within organizations. Users would much rather access and query data themselves. Its faster and more customized than relying on standard reports or submitting a request for an ad hoc report.
This trend is only going to grow as 2017 continues. Check out six ways that data analytics will trend over the next twelve months.
1. Data Gets Smarter
The Vs of big data are veracity, volume, velocity, and variety. Smart data involves removing volume, variety, and velocity from the picture and focusing on veracity. The idea is that by doing so, the value of that data to people and organizations increases in a meaningful way.
Smart data is useful and actionable. It involves weeding out the fluff and providing information that people can use to make decisions and predict trends. In 2017, brands will increasingly use artificial intelligence when analyzing data in order to improve its usefulness.
When that happens, the possibilities will be nearly limitless. Smart data can be used to create great customer experiences, optimize processes, and even improve product performance.
2. Data Gets Social
An astounding amount of data being created, transmitted, and consumed today comes from social media platforms. This includes photos, videos, updates, Tweets, blog posts, likes, shares, shared profile information, impressions, hashtags etc. This data comes from a variety of platforms including Facebook, Twitter, Instagram, WordPress, LinkedIn, and various user communities.
Many social media platforms are including analytics options in their dashboards. For example, Facebook insights provide users with the means to view information on the various ways that people are engaging with them. For example, a business owner can view the number of times people click into their Facebook page to read content but dont choose to like or share anything (impressions). This information can be gold for businesses who want to establish good leadership skills on social media.
3. Data Goes Real Time
Much of the focus on data analytics has been the use of historical data to predict trends. However, the increasing popularity of real-time infrastructures means that companies are now looking into the best methods to use data in real time. In order to get the most out of this, new information must be quickly and accurately mixed with historical data.
When this happens, the result is that the data being analyzed is both up to date and contains historical information as well. This means that the tools and processes used to analyze that data work that much better.
4. Data Gets Shared
This will be especially noticeable with companies involved in the internet of things. Because security and data privacy concerns are so significant with regard to these products, companies will begin banding together in order to share aggregate data. This will result in data sample sizes that are large enough to be useful for analytical purposes.
When this happens, machine learning can be applied. This will help to educate systems and designers to recognize patterns that could indicate security weaknesses or breaches.
5. Data Proves Itself (Hopefully)
As the popularity of big data increases so does the amount of revenue that companies are putting into big data projects. Thats something that is inevitably going to get the attention of upper management and bean counters alike. Over the next 12 months, project leaders and others will be asked more and more often to justify these expenditures. This will mean providing proof of return on investment and being able to communicate in real world terms why these projects are important and the benefits that will be seen in the future.
6. Data Gets Tied Down
The benefit of big data is that it provides everyone in an organization with the ability to use the same sets of data. However, just as access is not equal when it comes to operational data, nor should it be equal when it comes to analytical data. In 2017, more companies will review their data access policies as they apply to repositories and warehouses.
In all likelihood, these audits will result in businesses restricting access and ensuring that users only have permissions to get to the data that they need to do their jobs. One reason behind this is to prevent data exfiltration. This a method that people without authority are able to copy data that they are not authorized to over to locations where they can end-run those restrictions.
Conclusion
Big datas upward trajectory will show no signs of slowing down in 2017. However, in some ways, businesses will begin to take a more cautious approach to it. This includes ensuring that big data projects actually have great ROI.
