You can’t hardly read a business news story without seeing some sort of mention of big data and analytics. They’re key pieces of enterprise strategy, and 2015 was another year where we learned a lot about their role and how they can be used effectively.

A Sample of Popular Posts

All that data and analytical information doesn’t mean anything until it’s interpreted and used for an effective strategy. Here are some examples of how companies did that, and what it may mean as we look to the year ahead.

1) Finding the right option for predictive analysis is going to depend on a what a business needs. That’s what Virginia Backaitis found in her piece, Three Vendors Lead the Way for Big Data Predictive Analytics. Tweet to Virginia Backaitis.

...Open source software community is driving predictive analytics into the mainstream. Developers have an abundant selection of API’s within reach that they can leverage via popular programming languages like Java, Python and Scala to prepare data and predictive models.

2) Quite the fuss was made over Cloudera and MapR declining to join the Open Data Platform, as Virginia Backaitis found in Hey Cloudera & MapR: Open Data Platform is the Real Deal. Tweet to Virginia Backaitis.

“ODP by all appearances seems to be a marketing tactic, a distraction,” Matt Brandwein, Director Product Marketing at Cloudera, told us in an interview shortly after Pivotal Software announced ODP in February.

3) Microsoft has done a lot recently to make its software play well with others. That’s what Scott M. Fulton found with Microsoft’s New BI Tool Plays Nice, Even With Third Party Vendors. Tweet to Scott M. Fulton.

Power BI is a browser-based data visualization portal capable of connecting with corporate data using secured accounts, either on-premise or through select cloud-based vendors. It works with Microsoft's own Dynamics CRM as well as third-party vendors such as Workday, Google Analytics, Salesforce and Twilio, the platform for building cloud-based apps around customer VoIP and SMS messaging.

4) Cloudera’s CEO thought it was time to spike the football and declare victory. Not so fast, wrote Virginia Backaitis in Oh Please Cloudera: It's Not Game Over Yet. Tweet to Virginia Backaitis.

So is Cloudera doing well? Yes. Has it blown away everyone else on the map? There’s evidence to suggest that that’s not the case.

5) We’re awash in data. How do we get even more from it and use it to make smarter decisions? Virginia Backaitis has some ideas with Big Data Gets Smarter. Tweet to Virginia Backaitis.

While in a world of unlimited space and time we’d be able to shed light on everything that’s notable, we can’t because our world is a bit more limited. So what we’ve chosen to do this week is bring you the stories we would have covered in long form had we had the bandwidth.

6) Google Analytics is an excellent tool for understanding your traffic. But as Pierre DeBois discovered, sometimes you need to do some digging with Where Did It Go? Understanding Traffic Drops in Google Analytics. Tweet to Pierre DeBois.

Traffic changes for your website or app often happen as you begin reviewing analytic reports over time. But with so many reports and ideas, it can be a head scratcher to know where to start a diagnosis.

While no one set of tactics is the right way, there are a few great ways to get started in analytic reporting. The key is deciding if the traffic change is technical or from marketing influences on traffic.

7) Docker is revolutionizing how software and hardware data center architectures. That’s what Scott M. Fulton found in Everything You Really Need to Know About Docker. Tweet to Scott M. Fulton.

With yesterday's release by the Docker organization of open source tools for orchestrating the deployment of containerized applications anywhere from a data center cluster to a single laptop, the very definition of a business application is changing.

8) A lot of money is flowing to manage all that data. That’s the uptake from Hyoun Park in his piece, Big Data Gets Big Money for Big Reasons. Tweet to Hyoun Park.

Companies continue to improve user interfaces, enhance the usefulness of their own offerings, upsell new services, and reduce churn to create better experiences. To deliver these experiences, companies need to embrace predictive and big data capabilities to help build extremely personalized profiles for each user and each transaction.

9) Google finally threw in the towel when it came to creating its own social network and partnered up with Twitter. Jose Alvarez and Noreen Seebacher explored the deal with the article Google Confirms Data Sharing Deal With Twitter. Tweet to Noreen Seebacher and Jose Alvarez.

People will find out what you're tweeting quicker, thanks to a partnership between Google and Twitter. The deal, confirmed in Twitter's fourth quarter earnings call, will allow Twitter to provide Google with tweets as soon as they're posted to the social networking site.

10) Revenue is typically the main metric for determining how successful a digital analytics team is. That’s what Phil Kemelor tackled with How Do You Evaluate Your Digital Analytics Team's Success? Tweet to Phil Kemelor.

Nearly 75 percent this year’s EY Digital Analytics Benchmarking Survey respondents indicated that revenue generated from the digital channel was the primary method of evaluation. These numbers may surprise you based on what you experience daily, as well as what you see and hear from your industry peers.

11) The more you learn about digital analytics, the faster you can move in business as a Gartner report indicates many such jobs won’t be filled. Mark Hansen found a lot of tools from Google Analytics in his piece Google Analytics on the Line: Using Data for Better Insights. Tweet to Mark Hansen.

Before jumping to conclusions or settling on a tired strategy, marketers can turn to analytics to identify what’s working, what isn’t and where untapped opportunities may lie. Analytics can help you uncover your current audience is, where it's coming from and what content engages people. This data can spark ideas for new campaigns and improvements to existing strategies.

12) There’s another aspect of Google Analytics that can be a powerful tool: cohort analysis. That’s what Pierre DeBois found with How to Use Cohort Analysis Beta Reports in Google Analytics. Tweet to Pierre DeBois.

Analysts can introduce comparisons between segmentation features in the same manner as other Google Analytics reports. This includes default segments such as search and referral traffic, as well as custom segments set by the user. The Google Analytics solution gallery can also be used or imported into the cohort analysis. This lets users take advantage of solutions developed by other analytic practitioners.

13) It may seem like it’s hard to find a good data scientist. It doesn’t have to be, according to Joanna Schloss in her article That Elusive Data Scientist Might be Right Under Your Nose. Tweet to Joanna Schloss.

For most organizations, there’s no need to go outside the company to hire a data scientist. You’ve probably got a potential data scientist sitting right under your nose. All you need to do is empower him or her to play the role. After all, the term “data scientist” is largely a marketing creation. Though academic programs aimed at training data scientists are starting to crop up, there’s still nothing that formally marks one person as qualified or unqualified for the role.

14) Advanced analytics, as Virginia Backaitis explains, are the scalpels used by highly trained data scientists. She takes a look at the market leaders in Gartner's Look at Advanced Analytics Vendors. Tweet to Virginia Backaitis.

This year’s Magic Quadrant for Advanced Analytics report acknowledges that the tools are becoming friendlier to business analysts and citizen data scientists. But based on the reporting we’ve done around the various vendors in the past 16 months, we’re not holding our breath.

15)  Virginia Backaitis examined the rapid growth of Hortonworks in The Fastest Growing Software Company Ever. Tweet to Virginia Backaitis.

A Barclay’s report released in July that noted Hortonworks is expected to become the fastest growing software company — reaching $100 million in annual revenue in just four years from inception. Consider that Salesforce took five years to hit that number and that other vendors known for their skyrocketing growth like Workday, Tableau and Splunk all took six to nine years.