Quick advances in information science are opening up additional opportunities for organizations. They can extend their insight into their market, their clients and their business and recognize new open doors. The recent health, ecological and economic crises remind us that instability is a constant and that crises tend to overlap rather than follow one another.
The ability to dynamically draw insights from data to adapt and make the best decisions is a significant asset of resilient companies. At the same time, the volumes of data to be analyzed to achieve this objective are ever more dense and volatile. Technology must, therefore, absolutely evolve constantly to provide ever more precision and meet the needs of the “new normal”. To provide perspective, here are six trends that will shape the future of data analytics.
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Quick advances in information science are opening up additional opportunities for organizations. They can deepen their knowledge of their market, their customers and their business and identify new opportunities. Thanks to Artificial Intelligence, automating specific tasks that are impossible or too expensive to accomplish manually creates significant productivity gains in all areas: operations, marketing, sales, etc.
Data science enriches data sets, in particular, by identifying new axes of analysis or by grouping them according to “patterns”, counter-intuitive but eminently relevant categories. This information has a direct impact on performance and will feed into new-generation Business Intelligence tools. To realize its full potential, data science must spread across all departments of the company. This is why several technology giants invest massively so that their employees acquire strong data science skills, even when they are not specifically part of a team of data scientists.
Airbnb has thus created its own Data University; Amazon has embarked on the development of its Echo/Alexa artificial intelligence, the acquisition of multiple startups and the recruitment of hundreds of experts. In many companies where this is not their core business, there is still a lot of room for improvement.
Also Read: 7 Use Cases Of Predictive Analytics Set To Change The World
We know the risk of seeing models built on historical data be disrupted overnight. In the future, data analysis will imperatively be part of an agile dynamic, at the risk of losing all interest. Having a different tool and programming language for each team constitutes a serious obstacle to achieving this responsiveness.
The Python language, which has become widely democratized, is today becoming the common denominator between all data players: suppliers, modelers and consumers. Thanks to it, data scientists can create tools or discover insights that business users can use in a very short time. Python is particularly popular for data analysis and artificial intelligence but also backend web development and scientific computing. For all these reasons, in 2019, it recorded the most robust progression in terms of use according to the TIOBE Index.
Revealing sort approaches should be modified in increasingly more use cases. Up to this point, all BI advancements consisted of extricating from information bases and producing static reports that ran for the time being and were accessible the following morning or even a few days after the fact. Any alteration of the fundamental boundaries or solicitation for explanation would then require restarting the interaction without any preparation.
This is still a reality in many companies, which is in total contradiction with the need for responsiveness mentioned in the previous paragraphs. Having a real-time analytical system allows you to work with up-to-date numbers. If the first to request this type of capacity were traders and risk controllers in finance (in order to visualize risks in real-time, study churn, etc.), the need is now widespread across all business lines.
Beyond the real-time vision of a situation, we want to be able to simulate numerous hypotheses and calculate their impact on the KPIs in anticipation (e.g. what will be the impact if a customer cancels their order / the train does not make deliveries/demand drops by 10%…), and this is perhaps even more important than having a “live” dashboard.
To make decisions, it is no longer possible to wait ages for data that will already be “outdated” when it becomes available. There is a real need to be able to benefit from up-to-date predictive analysis and to be able to plan. Among the major drivers that make this development possible, we note in-memory (which increases the calculation speed of the tools tenfold) as well as cloud technologies that allow you to use resources on the fly, according to your needs, rather than having to buy expensive servers.
In view of the previous points, users, not only on the technical side but also in business teams, will have to navigate much more data than before and will have to quickly draw insights and make decisions from games of constantly evolving data. UX must, therefore, absolutely be integrated into the tools: they must become intuitive and attractive (where they are austere and static).
Components like “information narrating”, for example, outwardly giving importance to information; the intuitiveness of scientific stages and improved admittance to more data (having the option to focus in on the info, channel, update continuously, and so on); and normal language handling to question information and acquire applicable outcomes all the more rapidly, will be the leading figures of advancements toward this path.
The historical organization in independent silos, in force in many companies, has also created technological silos. For example, we can find banks with thousands of databases built on dozens of different systems stacked over the years, to which are added countless Excel sheets shared between employees. Getting insights into the status of the entire business from systems – CRM, ERP, TMS, WMS, etc. – that do not communicate or communicate poorly is highly complex and slows down IT teams, who must spend more time solving the problems this creates rather than offering innovative technologies to employees.
If these architectural issues are not addressed, businesses will not be able to reap the full benefits of the latest innovations in data science, AI, or machine learning. Their evolution will be partial, limited to particular subjects, rather than encompassing all the functions of the company.
The rapid evolution of mobile use.
It’s been a long time now that smartphones are no longer just used for communication. They are full-fledged terminals for using complex applications, including analytical ones, the use of which must be able to adapt to their limitations. The future of data analysis requires taking these new work habits into account in the creation of today’s analysis tools.
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