It’s easy to say Big Data. The universe of Big Data is a multifaceted and diverse world within which a timely identification of the most relevant trends is crucially important. In order to understand both how to address investments in the best possible way – something which is certainly not a minor aspect – and to support the development of the right skills system in organisations.
This is the reason why setting forth (with an easy pun) the “big Big Data Trends” for 2017 is not only a mere pre-Christmas formal exercise, but meets an actual need within a complex environment such as that of big data analysis: a timely reaction – both in terms of investments and of skills – to a context change where the proper management of the business intelligence of an organisation often determines its health status.
Many have already set forth and will set forth their “best predictions” for 2017 over the coming weeks, but one that is particularly interesting is the “BI Trend Monitor 2017” paper by the German BARC (Business Application Research Centre) research and analysis institute, which is based on 2,800 interviews with users, companies and industry experts. By analysing business intelligence trends, the study essentially defines – because of complementarity – the dynamics applicable to the world of big data analysis as well. Its main results? Here they are in a “nutshell”:
1. Data Visualization and Discovery
The topic of techniques (and technologies) of data visualization and discovery, according to the panel established by the BARC analysts, is the most important one for 2017 (and it was also so – albeit less prominently than others – for 2016). In other words, identifying the right data is of little value if then you do not know how to visualise them in a comprehensible manner. The centrality of the data visualization and discovery systems is even more relevant if we consider that the issue is considered all the more important the more you look at countries where the culture of using Big Data is more consolidated. In short: the more the awareness of the subject increases, the more one realises how important this point is.
2. Self Service Business Intelligence
If visualising data is considered a central point, so is… doing so with the correct data. The Self Service Business Intelligence concept – i.e. the ability to supply tools to users capable of allowing them to be independent when managing services offered through Big Data Analysis instruments – represents a focal point. A point which highlights how a strong element of the Big Data Analysis instruments must be that of providing the right answer to the right user at the right time. So: nothing different from what anyone who deals with knowledge management has been preaching for years, but it must be said that inferential statistics has made things a little more complex, and when the going gets tough…
3. Master Data/Data Quality Management
It may seem trivial, but if you think about it, it isn’t. In a context where the number of data increases dramatically and analysis models sometimes tend to abdicate to the “why” rather than to the “what”, being able to be certain that we are employing correct data is not entirely obvious. This is the reason why, on the one hand the concept of a “single point of reference” becomes central, and on the other data quality management becomes a key function. So: working based on deductions is fine, but at least the basic data should be correct, otherwise you risk losing all control of the situation.
4. Data Governance
If data are to be useful for something, obviously, this something must correspond with supporting the organisation during its operational and strategic decisions. This is the reason data management must be linked to process management. In this case also, a seemingly trivial concept hides an enormous critical situation for those involved in data governance. Connecting the datum governance with that of the processes which generate it (and with the processes to which it is applied) means inextricably connecting data strategy to business strategy as a whole. So: dealing with data governance is tantamount to supporting business strategy, not only through data analytics activities, but through an approach that places the datum at the centre of strategic business choices and which makes it a key element. On the other hand, where it is true that we live in the era of information, we cannot forget that this information is based on the data that determine it.
5. Predictive Analytics
“Datum, tell me something I do not know”: this is what the experts interviewed by Barc seem to be shouting when they argue that the centrality of predictive analysis techniques is constantly on the increase. Arithmetic is enough to do sums and averages; the real need – when it comes to Big Data – is not so much that of analysing existing information, but rather of developing new levels of information from that starting point. In particular, information which may be used for forecasting. Sales, stock market trends, weather forecasts. Forecasts of forecasts. So, in conclusion: within a context whose future is increasingly complex, data and analysis techniques must help us make it a little simpler. Will they succeed? Hopefully the answer will not be 42