Wednesday, December 28, 2016

Data and Analytics Trends for 2017


Data and Analytics Trends for 2017

No, not in the sense of fleeting fads and fast concepts.

What we’re talking about are areas in tech that stood out this year.
Polyglot Persistence, Analytics 3.0, AI and machine learning all left a lasting impression.

Here’s a closer look at these trends and why they should be on your radar.

The rise of Polyglot Persistence
The concept of one-size-fits-all should be left to muumuus and ballcaps not datasources. Variety- as they say, it’s the spice of life. Not surprisingly, we’re hard-pressed to assume companies can manage all aspects of all data through a sole channel. That’s like saying you’d be able to store all your kitchen gadgets in a slim drawer- it only works if the design fits the concept. According to Technologies & Heller (2016), “Each database is built for its own unique type of workload. Its authors have made intentional trade-offs to make their database good at some things while sacrificing flexibility or performance in other categories.” So do as the Romans do...wait, what do the Romans do? I kid, but seriously, let’s talk about joining forces sources. Our friends at dummies.com tell us polyglot persistence ”...is used when it is necessary to solve a complex problem by breaking that problem into segments and applying different database models” (Hurwitz, Nugent, Halper, & Kaufman, n.d.). Once we have our sources- anything from RDBMSs, NoSQL, REST API’s, don’t you have to ETL it all over the place to get insights and access to that data? Enter Analytics 3.0.

Analytics 3.0
Reporting, analytics and self-service analytics has been around for awhile. What has been changing are the methods companies and people use to get to their final destinations. Let’s take a quick trip down memory lane and see where we’ve been as well as the illustrious yellow-brick road we are about to journey on…

BI 1.0 (A blast from the past)
Oy. This is is painful. Excruciating, debilitating, and time-consuming. What is it that has so many organizations screaming, “Uncle!”? Complex, back-end prep, that’s what. Once deployed, engineers and architects spend days, weeks, and sometimes months to painstakingly process raw data. Enter ETL. Extremely Tough Life? Well, yes and no. It’s a tough life for those charged to Extract, Transform, and Load heaps of information. I don’t wish this on my worst enemy...well, that’s not entirely true. I have seen this soul-crushing process and it is definitely working harder not smarter. Now, let’s be fair- at one point this business intelligence process was innovative, nay brilliant, nay revolutionary! But so was the cart and horse. For the first time, data could be recorded, aggregate, and analyzed (Davenport, 2013). Luckily, there was an proverbial throwing of the hands which led us to a new frontier...        

Big Data 2.0 (Volume, Velocity & Variety)
Imagine you’re in the middle of a labyrinthian building. People talk in hushed tones while the rhythmic keystrokes of well-worn computers drum on. You ask yourself, “What are they up to? And what is NoSQL?” You my friend, have just landed at a major crossroad- you can either continue with your legacy BI tool, or you could embrace this new wave of unstructured data. Unstructured? I like structure. I live for structure. My boss will can me if I don’t structure! Calm down, tiger. Data storage needs depend on many variables notably the 3Vs: volume, variety, and velocity. No doubt, all  aspects of the 3Vs and Big Data will cross-pollinate just to what degree. Volumetrically, data storage requires scalability- relational stores just can’t cope (Media, 2012). The velocity of incoming data (as well as the sheer variety) are better served with NoSQL databases. Relational stores need a large amount of work before insights can be of any real use  (Media, 2012). NoSQL data sources are agile and capture data that is in constant flux. NoSQL options complement traditional SQL based offerings with efficient options for unique data needs.

Current Generation (Dude, where’s my data?)
Data is everywhere. From the thermostat controlled via smartphone to the video camera that captures traffic patterns, data is all around.  The current version of analytics must address both data harnessing across multi-structured data sources  as well as actionable insights.

Analytics 3.0 moves further down the road from data that simply informs to data that predicts and prescribes actions. Leading edge companies are already using machine learning to understand how their customers actually use their products, services, etc. Then they use AI to personalize experiences so a person will actually to do something, buy something, sign up for something...you get my point. This will only accelerate in 2017. According to Ferreira (2015), our new era will be “the driving fact behind not only operational and strategic decision making, but also the creation of new services and products for companies.”

What is AI & Machine Learning and Why You Should Care
As humans and computers intersect, the need for advanced technology increases. While often used in the same sentence, these two concepts are not synonymous. Nor are they mutually exclusive. Heavy hitters like Google, Facebook, and Amazon are making AI and machine learning more widespread (Bell, 2016). In essence, machine learning uses previous experiences to influence future decisions. Artificial Intelligence is the process of making machines smarter or more intelligent. For example, when we make a typo, machines suggest a replacement and remember responses for the future. Alternatively, look at Computer Science, AI, and machine learning as a series of umbrellas. According to Intel’s Nidhi Chappell, “The way I think of it is: AI is the science and machine learning is the algorithms that make the machines smarter.” (Bell,2016). As reported by Fagella (2016), “only one percent of all medium-to-large companies across all industries are adopting AI”. AI adopters typically see increased revenue via faster image interpretation, documentation and data entry, and increased productivity by 25% (Wilson, Sachdev, & Alter, 2016). And that’s just the tip of the iceberg! Imagine all the time we free up when we use machines to work for us! My cereal-filled mornings with The Jetsons are looking less nebulous and more reachable everyday.

Did we miss your top trend for 2017? Leave your thoughts and comments below!

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References

Bell, L. (2016, December 1). Machine learning versus AI: What’s the difference? Retrieved December 21, 2016, from http://www.wired.co.uk/article/machine-learning-ai-explained

Davenport, T. H. (2013, December 1). Analytics 3.0. Retrieved December 20, 2016, from Analytics, https://hbr.org/2013/12/analytics-30

Faggella, D. (2016, September 30). Valuing the artificial intelligence market, graphs and predictions for 2016 and beyond -. Retrieved December 21, 2016, from Tech Emergence, http://techemergence.com/valuing-the-artificial-intelligence-market-2016-and-beyond/

Ferreira, T. (2015, December 09). What is Analytics 3.0? Retrieved December 20, 2016, from https://www.quora.com/What-is-Analytics-3-0

Hurwitz, J., Nugent, A., Halper, F., & Kaufman, M. Big data and Polyglot persistence. Retrieved December 19, 2016, from Engineering, http://www.dummies.com/programming/big-data/engineering/big-data-and-polyglot-persistence/

Media, Or. (2012, January 19). Volume, velocity, variety: What you need to know about big data. Forbes. Retrieved from http://www.forbes.com/sites/oreillymedia/2012/01/19/volume-velocity-variety-what-you-need-to-know-about-big-data/2/#7890f71f7c1d

Technologies, R., & Heller, B. (2016, March 25). Analytics 101: Choosing the right database. Retrieved December 19, 2016, from https://reflect.io/blog/analytics-101-choosing-the-right-database/

Wilson, H. J., Sachdev, S., & Alter, A. (2016, May 3). How companies are using machine learning to get faster and more efficient. Retrieved December 21, 2016, from Harvard Business Review, https://hbr.org/2016/05/how-companies-are-using-machine-learning-to-get-faster-and-more-efficient