Business Capgemini: CTO Blog

Kickstart My Year: 7 trends in Insights & Data for 2017

Author

Ron Tolido

January 6, 2017

Still remember our rogue kickoff for 2016 with seven key trends in the world of BI, analytics and AI? Pretty accurate they were, no? Anyway, much has changed in just a year and we have much to look forward to in 2017, with data being the undisputed driver of any serious business change effort. Just like last time, we have carried out a quick survey among our global Advisory & Architecture community to identify the hottest topics in insights & data – the ones that we feel will have a real impact on businesses.

In fact, some of the topics also contain a sneak preview of our TechnoVision 2017 release – due at the beginning of next year.

It’s a motley crew of trends here, as you’ll see, with major themes such as automation, enterprise-scalability, cloudification and (surprise) the rise of Machine Intelligence making it to the top of the pile.

So – Kickstart My Year – here’s what you need to know for 2017:

1. BI Curious

The recent wave of technology innovations – driven by Big Data – is now starting to enter the mainstream of ‘plain’ BI as well. The world of reporting & dashboards and descriptive & diagnostic analytics is increasingly benefiting from a powerful mix of cloud, open source solutions, self-service platforms, advanced visualization, collaboration tools, automation, cognitive interfaces and AI-assistance. A pretty compelling picture to explore with both convincing cost benefits and productivity as well as improved agility and effectiveness. It will give the seemingly established BI landscape a radically modernized, vanguard face, enchanting business users and solution developers alike.

2. The Empire Strikes Back

As often is the case, the disruptive innovations in data are coming from the open source and start-up communities. And it has taken the major industry players arguably way too long to catch up. But rebound time, it is now. Technology leaders such as Microsoft (look at how they embrace the open source ecosystem), SAP (bridging Hana and Big Data platforms, acquired their own Hadoop-as-a-service provider), IBM (yes, there is life outside Watson) and SAS (going cloud and Big Data with their new Viya platform) are on full speed. And the interesting thing? They are merging their enterprise-scale, high-productivity tools with innovative, new technologies. The best of both worlds. In any galaxy, really.

3. Ceci n’est pas un Éléphant

Don’t know what an elephant has to do with Big Data? Go work on your street credibility. For all the others, it’s good to realize that nowadays the Hadoop ecosystem is no longer just about – well – Hadoop. Although arguably the entire Big Data revolution started with the abilities of the Hadoop Distributed File System (HDFS) to store and provide access to huge amounts of data in any structure or unstructure, it’s now the powerful set of analytical tools on top of it – such as Spark, Storm and HBase – that really provide the new value. Expect your shiny Hadoop servers therefore to disappear sooner or later. First into the cloud, provided as a scalable, on-demand service, and ultimately being replaced by other – even more effective – storage and access services.

4 At Your Service

One does not just become insight-driven by hiring data scientists and data engineers. Every business person should become a bit of a data expert, perhaps even a ‘citizen’ data scientist. The best insights are created in near proximity to the business and for that, data must be discovered, prepared, analyzed and visualized by the business. It requires a highly automated data ‘pipeline’ that gives agile access to the right data – all the way from its ingestion, while ensuring security, privacy and enterprise quality. It also requires easy-to-use, self-service tools that power the business to take matters into their own hands and work together to become insight-driven.

It will certainly also depend on an increasing level of Machine Intelligence, to help business users to identify and prepare exactly the right assets in their corporate data lakes (watch Informatica for this with their Live Data Map). Expect to hear more of the concept of the Data Concierge, as your intelligent ‘one-stop shop’ for data.

5. You Do The Math

We know, we know. Data science is not necessarily about math. But it sure sounds good as a trend headline, doesn’t it? And although the BI people rightfully are claiming their part of the new data landscape, the future is definitely in algorithms. Algorithms that help make much better-informed decisions, predict what will happen and even prescribe what should be done to achieve objectives in an end-to-end business. An eclectic catalogue of algorithms could be the most differentiating business asset, whether pertaining to the customer experience, internal operations, human resources, risk, fraud or physical assets (IoT almost equals algorithms).

It requires understanding the full analytics lifecycle, building the new skills that are needed and gradually establishing a true analytics culture across the organization. In practice, a combination between a top-down vision of what the enterprise wants to achieve through algorithms, and a hands-on, applied way of getting busy with early applications and results will work best.

And there is a quickly growing market of sector and domain algorithms out there as well. Algorithms that are ready to be used, right out of the box. So you don’t need to science your way out of this all on your own.

6. The P&L of Trust

Particularly when you are in Europe, the upcoming General Data Protection Regulation (GDPR) is likely to have a major impact on your insights & data plans for 2017. If only to comply by the 28th of May 2018, as any fine may be up to 4% of your organization’s yearly revenue. It’s what some might consider a valid business case. A lot of work may need to be done, in terms of securing both the privacy of individuals and the security of their personal data. It may involve new technologies, new roles, new organizational structures and a thorough overhaul of the existing data landscape.

Seems like a nuisance, admitted.

But on the bright side, companies that do it well have the unique opportunity to reach out to their customers, be transparent and conversational about the use of their personal data and – ultimately – use trustworthiness as a competitive differentiator. From that perspective, any strategic decision in the data context should be considered in terms of its impact on the bottom line of trust. If we’d been completely without inspiration, we might have called this crucial trend ‘Trust Is The New Oil’ (but of course we didn’t).

7. Max Machina

Breakthroughs in particularly deep learning and raw computing power are fueling the renaissance of AI and Machine Intelligence (there is Machine Intelligence 3.0 now; guess we had that coming). And it’s so much more than yet another drop in the ocean of hypes. Of course, you can go for the Full Watson and aim for a drastic overhaul of your business model. But this will take time and a relentless focus.

In the meantime, you may want to consider exploring conversational technologies (such as Facebook’s Messenger platform or the Microsoft Bot framework), embed AI in your business applications (Salesforce would agree), apply cognitive technologies to your unstructured text (for example to understand what is in a complex contract with RAVN) and even use it to optimize your own IT processes (smart automation won’t be able to do without). You know, the latter if only as a way of drinking your own champagne.

And now we’re on the topic anyway: by all means, thoroughly enjoy the holiday season! We’ll see you on the other side for what promises to be a breakthrough year for everything insights & data.

This article was written by Ron Tolido from Capgemini: CTO Blog and was legally licensed through the NewsCred publisher network.

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