Business intelligence and big data have an unresolved conflict. 

2016 was slated to be the year that big data projects finally delivered on the promises of profitability. Yet, 12 months later we’re still sitting on piles and piles of data as big data solutions languish in the corner. In fact, upward of 80 percent of data lakes and data science projects have failed to deliver their expected ROI. 

As we start fresh in the new year, businesses must embrace new strategies for finding insights in both unstructured and structured data, otherwise we risk yet another year of big data projects that under deliver. 

The Year in Analytics

The hype around big data in the past year obscured the fundamental need for companies to connect to and analyze all data. All data has potential value and all data is needed to ensure a complete view of the trends impacting your company and customers.

Businesses must merge both structured and unstructured data sources that live behind the firewall, outside of the firewall and in the cloud. This includes sources in data warehouse, social media content and application data. It’s not hard to imagine how all these disparate data sources that reside within different realms of responsibility can cause confusion. 

Additionally, over the past year IT began pitching data projects over the fence to business analysts. 

While the intention of democratizing access to data was good, the approach failed to deliver. To cope with the influx of data requests, businesses equipped business analysts with end-user data prep tools hoping that it would solve problems and accelerate data-driven decisions, but that approach just moved the problem. 

When data is handled improperly or with the wrong tools, governance, accuracy and security are sacrificed. 

In 2017, let’s continue to strive to get it right. And to do that we should bounce the ball — and the budget — back into IT’s court. 

4 Analytics Resolutions for the Year Ahead

1. Culture, culture, culture

You hear it everywhere: IT and business stakeholders are feeling like they were not consulted during major technology decisions. 

Builders of analytic systems or data infrastructures are visionaries. To see your vision come true via adoption, you must address the stakeholders and customers that you are serving. 

Businesses that succeed at becoming data-driven focus on meeting the needs of the culture, while also sharing and evangelizing their solutions to all stakeholders. After all, the more people use and improve upon your vision, the better the outcome. So remember, in addition to the visionary, you must also be an evangelist.

Learning Opportunities

2. Think hybrid (because it's here to stay) 

Hybrid approaches that fuse both BI and big data tools are here to stay. 

Over the past year, we’ve seen organizations sacrifice success when they throw structured data on unstructured platforms and expect it to do what they want. Relational databases and sources will continue to power BI — and businesses cannot succeed in unstructured data analysis without them. 

In 2017, choose and design a hybrid solution that allows you to deliver agile solutions that address analytical insight with all relevant data.

3. Use the right tool for the right problem

The ability to reconcile data from both SQL and NoSQL environments is paramount. Organizations need tools that can manage, merge and analyze the intersection between unstructured and structured data. 

The fundamental differences between tools, whether it’s a BI tool or data visualization tool, revolve around the needs dictated by data variety, volume, veracity and velocity. So consider these needs before selecting the tools you’ll adopt.  

4. Minimize maintenance 

Maintenance is the most difficult part of sustaining a data analytics strategy, which is why businesses should remember that the do-it-yourself approach also means “maintain it yourself.” 

If you build solutions from scratch, you are going to have to maintain them for all of perpetuity — an insurmountable task for most businesses. Instead, IT departments should look at available solutions and then customize as appropriate. After all, some say building it is all the fun — why not let someone else do the maintenance? This frees up your organization’s precious resources to do other valued work.

Before your organization sets aside budget to buy yet another data analytics tool or hire 10 more consultants, revaluate your strategies for choosing data analytics solutions and embrace these resolutions. Most importantly, embrace the convergence of structured and unstructured data to power data analytics strategies that succeed over the next year. 

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