Gartner data analytics trends: Back to where we started?
The “Gartner Data & Analytics Summit” took place in Frankfurt on 23 and 24 October 2018.
My fellow director Martin and I attended this event to learn about current trends in data analytics. Buzzwords such as artificial intelligence, machine learning, cloud analytics and NPL (Natural Language Processing) are on everyone’s lips and it’s obviously important for us to gauge the current status quo and look at what the future could hold.
Among other things, the pace of change within the digital world is exemplified by Gartner’s hype cycle, where the term “blockchain” is already on the downward curve – even though only very few practicable, comprehensive implementations and application scenarios are evident, even among digital pioneers. (Incidentally, this impression was also reinforced at the DSAG annual conference). Obviously, we need to ask how trends are identified which have a lasting impact on the issues of relevance for us within the context of data analytics. Before explaining the trends, let’s take a look at past and present:
15 years ago: Users in specialist departments were to be encouraged to analyse data themselves.
Why is this blog post entitled “Back to where we started”? When we became involved in the data analytics business just under 15 years ago, the situation was this: Although analytics software such has ACL™ and IDEA was available, it was only available as desktop software. Companies endeavoured to establish the use of data analytics by encouraging employees to use ACL™ Desktop, load data from systems into the software, and perform analyses.
This often failed for a variety of reasons. The software was often too complex for users without an IT background. Manual analyses, linking different data sources via homogenised primary keys, JOIN logics and the like proved too taxing for users, who were also unable to find time to use analytics tools on a regular basis in their day-to-day business. So although many analytics software licences were in circulation, they were very much under-utilised.
2010 -2015: Monitoring and BI platforms become established
This was a decisive factor in our successful business model. Using server solutions to centralise analytics, ensure data access to e.g. SAP® data and establishing predefined analytics content, we made data analytics a usable tool for our customers even in day-to day business. Our solution can therefore be assigned to what Gartner refers to as the “BI era”.
2015 to date: The mix of traditional BI and self-service platform becomes a success model
According to Gartner, however, these BI platforms are becoming less important, with a dramatic shift away from pure BI monitoring. It believes that currently, monitoring via information portals and dashboards is almost as important as explorative analytics using analytics workbenches and self-service platforms. We concur with this, as concepts such as our dab:AnalyticSuite enabled us to move away from a “reports only” approach some while ago, providing users with a database for various business processes which they can use to perform their own analyses without having to start from scratch when combining raw data.
Short and medium-term outlook 2018-2021
For the next three years, Gartner believes that these self-service platforms will be supported by data science and artificial intelligence/machine learning. We share this opinion, and are already working towards this with new products such as dab: AnalyticIntelligence. There will always be a need for making rule-based queries in the form of certain reports, especially in the context of the internal control system. Alongside this, however, increasing data volumes and the challenge of obtaining answers to questions that aren’t even yet known call for new data analytics methods. These will not replace traditional data analytics, but rather complement it at important points.
Looking further into the future
Without being specific as regards content, this is referred to as the “era of artificial intelligence”.
A trend which became clear at the event is the increase in “business-authored content” via self-service platforms. The aim is to encourage end users to conduct independent analyses and create new analytics content, because achieving the necessary scope and full penetration of all business processes using data analytics is no longer manageable by other means. Data analytics is not a business process but a part of all business processes. The assumption, therefore, is that we should no longer try to “bring data analytics under the IT umbrella” and that “users should not rely on IT for writing reports”. In turn, this naturally requires IT to have confidence in specialist departments and the creators of analytics content.
... Now for a critical assessment: quality aspects
However, if we decentralise and so to speak democratise data analytics, how do we secure the requisite quality and how can we ensure that analyses which are created decentrally by users are correct? After all, they form the basis for important decisions, or can be considered breaches of internal regulations, etc. On this point, various presentations made it clear that Gartner sees a need for training and certification processes. This was also referred to as “empowerment” of users, akin to a driving licence. Companies must ensure that the rules and methods are sufficiently well known before they are used for data analytics.
... and continuing our critical evaluation: resources
While all this may be understandable and plausible and sound very good, it does bring us to the title of this blog post “Back to where we started”. To anyone who has been in the business as long as we have, it will seem as if they are being transported back in time 15 years. We worked hard to bring data analytics to specialist departments; users were trained in analytics tools, certificates were acquired, not only in the use of analytics software itself but also in developing an understanding of business processes and data structures. So far so 2005.
Of course, it can be said that analytics tools are now more powerful and more user-friendly, and that due to increasing digitalisation, users have become more familiar with IT and data in recent years. However, customers for whom we provide data analytics support often complain that it is difficult to provide IT departments with sufficient data analytics capacities in terms of human resources. So it will be even more challenging to transfer these analytics capacities to specialist departments as part of the above empowerment process, since this will obviously require a significant headcount increase. Obviously, this is not just a question of costs. There is also in particular a need to develop the required affinity towards analytics, not to mention the issue of qualifications. Or to put it simply: “Where will we find all these people?”
Conclusion
As a personal conclusion, our experience leads us to concur with many of the statements made and the trends identified by the speakers, even if some key points such as the question of resources have to be viewed with a critical eye. In terms of product portfolio, our dab:AnalyticSuite solution ensures you are ideally equipped as regards both traditional BI, monitoring and dashboarding, and also support of self-service platforms through our pre-defined data models, which provide optimum support during execution of independent, decentralised analyses. Since we prepare and combine the data in advance, it is also possible to rule out many traditional sources of errors in advance.
With regard to “explore the unknown”, we are driving non-rule-based data analytics with our dab:AnalyticIntelligence solution, so that here, too, we can go beyond traditional reporting and offer our customers totally new findings in relation to their data landscape.
And of course, empowerment is an integral part of our day-to-day business, which we have been conducting successfully for almost 15 years. Gartner’s description of this term is what drives our corporate vision: Making analytics fundamental.
For you as customer, this means that in us you have an experienced partner at your side who will provide both software and services to guide you towards the future of data analytics.
Please contact us at any time if you have questions on this topic – we look forward to interesting and dialogues with you.