2016 and Business Analytics: prepare for a smashing year

The end of the year is always a time to reflect, but also a time to look ahead and think about what might be different or innovating next year. Reflecting on 2015, three things immediately come to mind:

  1. Interactive self-service business intelligence has definitely landed and earned its permanent place. Every top-100 customer I talked to, has self-service business intelligence in its BI Strategy plans

  2. “Traditional” business analytics, as in managed reporting and dash boarding, is not sufficient anymore for full performance management; a closed loop portfolio of analytical, predictive, planning and GRC information is becoming a necessity in today’s management of processes and business flows.

  3. The value of In-memory platforms is now being recognized by the leading companies. They massively adopt in-memory platforms to not only run their core applications, but also to integrate business data and facilitate analytics.

Looking forward you will agree with me that analytics is heavily influenced by the readiness of organizations to adapt change. A change that is so needed for them as a result of the Digital Transformation. Connected economies and networks, data being available to us at any moment at any level, and sensor techniques allowing for new business models; they all heavily influence our needs for insights. As such they heavily influence the 2016 trends for business analytics.

Did Tableau loose their heads?

Like you, I also did the “Google for: BI Trends 2016 – exercise” and was both shocked and amazed by the work that our friends from Tableau created: amazed by Tableau’s marketing department who succeeded to have 80% of the first 20 hits all referring to the exact same article. Though it seem to be all different articles, if you read them well, they all cover identical things. Highly impressive work of their marketing department!

Shocked however by the lack of insights these identical articles cover: They are all headlining BI Trends 2016 though my feeling is that this should be BI Trends 2014. “Governance and self-service become best friends”, it says. …. Dear people from Tableau, self-service business intelligence can only EXIST by the sake of data governance. If self-service BI is not governed properly, there is no sense for it. Similar goes for the trend mentioned as “Data Integration gets Exciting” which was something everybody focused upon in 2012.

Luckily there are also some trends mentioned in Tableau’s postings that are actually expected to raise in 2016. Personally, I can only reflect on what I see and hear talking analytics with key customers every single day. For me these discussions are the food for thought on what we can expect in 2016. Let’s have a look on them.

Analytics projections on 2016

Overseeing technology evolutions, industry trends heavily influenced by the Digital Transformation, but most of all listening to the plans that my customers have, I can distract 5 trends for Business Analytics in 2016:

Self-service BI becomes commodity

Governed self-service business intelligence will further find its way to all echelons of organizations. Reason is simple: business users finally have the opportunity to drive analytics in their organizations. Where 2015 was the year of adopting self-service BI, 2016 will be the year of the massive roll-out. Self-service business intelligence is becoming commodity in 2016 with the number of business users growing rapidly. From a functional perspective, the success of self-service business intelligence is greatly determined by its capabilities to:

  • interact with the user.

End users being able to interact with the massive amounts of various structured and unstructured sources of information, makes self-service being adopted quickly.

  • make data and insights easily visible.

Business users really recognize the value of making insights visible. The simple but clever idea of using visualizations and analyses to create your own stories – storytelling and infographics – is successful. Very nice examples are GEO driven stories and dashboard but also the ability to use D3 Open source visualizations. Together with interactivity, this makes self-service BI a stunning combo: I have mentioned before that “your meeting will never be the same” ……. Simply by the fact that “your meeting won’t be the same anymore”; we now use interactive, visualized insights to discuss and monitor the heartbeat of our company in real-time!

  • agility towards new and ever changing data

A third success-factor (what’s in a name J) to self-service BI is it’s agility towards ever changing data sources and structures. Allowing business users to really simply acquire and enrich new data and use it for analyses, is a huge value-add. Bearing this also applies to big data using in-memory computing, even strengthens the agility business users are looking for.

Business will embrace the portfolio loop

I have made my point on the importance of the closed loop portfolio in earlier blogs. Every key customer I have met last year that is willing to embrace Digital Transformation, is seeking for an integrated and governed platform to analyze, plan, predict and assess risks in a constant and permanent loop. On purpose I use the word ‘integrated’ since here were the difference is made: customers seek to have real-time integration between their business analytics, their detailed planning, and the predictive models that affect for example product mix or pricing strategy. The integration also needs to be on operational financials and if needed towards risks and compliancy cases.

Many of my customers have accomplished the above on a near-integrated level that is not real-time, by using individual components that access each other’s data. Products like Cloud for Analytics are revolutionary here since they provide the closed loop portfolio covering real-time, interactive integration on all mentioned areas. Markets have been waiting for this quite some time and are eager to adopt. It allows them to interact with market fluctuations that speed up due to the Digital Transformation. Look at the examples I described for the retailing sector to understand the scope of the closed loop portfolio.

We are ready to really analyze Big Data

Big Data discovery – find an interesting article here – is now ready to be implemented. We all used 2015 to understand and practice how to connect to big data sources like Spark clusters, Hadoop, Cloudera, MongoDb and others. In parallel software vendors ensures the analytics components were prepared to do so not only in the way they connect to the sources, but also how to liaise with core data. The latter refers to enrichment tools like SAP Vora but also to techniques like data blending. We are now all set and start adopting big data discovery to use its insights in our company’s performance management. And that predictive analytics components do now have the capabilities that even “normal people like you and me” can utilize them, is the cherry on the cake.

Cloud BI accelerates

Timo Elliot wrote about “who’s afraid of Cloud Analytics” discussing a kind of unfairly anxiety organizations might have for BI running in the Cloud. His article is very interesting and confronts us facing the facts that lots of data is already coming from outside, so why not heaving your own data there. He also arguments business users shouldn’t really care on where their data is.

Fact is that BI in the Cloud has a number of undeniable advantages like state of the art performance and functionality and highly limited maintenance while securing your own data by all means. It means business users can focus on what they are good at and positioned too: collecting insights and act intelligently upon them.

Referring to Forrester reports (“36% or respondents had already replaced, or are planning to replace, their on premise BI with SaaS BI, and 31% had chosen SaaS BI to complement their existing BI, or intended to do so in the future.”), I conclude that companies are becoming more open towards Cloud BI and as such we can expect accelerations in 2016.

Operational BI says “Hello World”

To be honest, I expected a bigger footprint for operational business intelligence, already in 2015. Reason for my expectations is the widely spread of implemented in-memory platforms that really got the momentum in 2015. The calculation power of these platforms is so high, it often allows to run analytics directly on the operational data instead of creating a dedicated data warehouse environment.

Conversations to my customers tell me that companies were waiting with operational BI for the very simple fact that analytics has become a core activity with them. They are cautious changing their model into operational analytics because they rely on their analytics so intensely: companies prefer to first implement their operational applications onto in-memory computing. In a second phase they shift their existing (!) analytics onto in-memory computing. Only in a third phase they consider to simplify their analytics and run them directly on the operational apps. It is a bit the safe-route but very understandable.

As a trend for 2016 we can clearly see that especially new analytics projects, will “say Hello” to operational BI being developed directly on the operational data. The platforms are there and the simplification is the driver.

Things to keep an eye on

Is there more stuff coming up in 2016? Definitely, but for me – and especially the customers I work with – the above mentioned 5 trends are the ones that really matter. Some things however to keep an eye on in 2016 are:

  • Collaboration within analytics – but even more important: within the closed loop portfolio – becomes more and more important. We expect further focus the topic of collaboration and workflows by both users but also software vendors.

  • Predictive analytics will get further momentum and will closer integrate with analytics. We have seen predictive analytics become useful to end users during the last 2 years.

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