More and more organizations have started to appreciate the value of business intelligence (BI) when it comes to their decision-making process. The past few years have seen BI systems go through different evolutions. Here are some BI trends that we believe will have a great impact in 2019 and beyond.
A study conducted by Gartner predicts that by the end of 2019, analytics output from self-service BI software will surpass the analytics output of data scientists. Self-service business intelligence enables business users to access and manage data even without an extensive background in data analysis or BI. This model gives business users more freedom to create their own data reports, without the need to send data requests to in-house IT staff or data scientists.
This will decrease the burden placed on the IT staff and allow them to focus on other tasks that may be more beneficial to the organization. Giving business users the power of BI will increase the efficiency and agility of the organization.
In order for organizations to compete with today’s data-driven culture, business users will need to have the ability to make decisions faster. This increase in efficiency and agility from the use of a self-service BI model will allow them to do so.
Gartner predicts that by 2020, more than 40% of all data science tasks will be automated which will result in increased productivity. This is already happening as vendors are continuously trying to automate parts of the analytics process. As business begins to recognise the value in the use of BI systems, the demand for data analysts and data scientists is increasing.
However, as the Analytics Institute has noted, there is a major skills shortage in data analytics. organizations realise that they need to find ways to perform more tasks with fewer resources and automation is a great way to shorten the time to value. In 2019 and beyond, we can see the trend in automation increase rapidly to compensate for the skills shortage.
Data scientists often use very complex tools to create on-demand reports for managers. However, these results can often be too difficult for users who do not have a background in data analytics to understand. This has resulted in a need for more narrative-driven data: data that has been interpreted and contextualised so that it’s easier for the users to understand reports as well as the reasoning of the data analyst. “Data storytelling” has become an integral part of the analytics process. Data visualisation – using flowcharts and graphs – properly convey to decision-makers what Gartner Research VP Neil Chandler referred to as “hidden truths.” However smart a BI tool is, if the data analyst cannot communicate the results to the user, the results will not be useful. There are already a few start-up vendors that are trying to bring storytelling tools to the market, but the products are very small and none of the industry giants has a fully functioning storytelling product as of yet. In 2019 and beyond, we can expect some of these industry giants to start making moves into this market.
Natural language querying (NLQ) allows users to interact with data. BI vendors, such as Microsoft Power BI, Qlik and Tableau, already have text-based NLQ where you can type in a question and get the result. This interface provides non-technical users with the ability to ask questions naturally. Natural language with machine learning enables BI systems to gain a deeper understanding of the organization based on its data as well as the types of questions their users ask. Users will be able to use natural language to have conversations with the data visualisations without the need to rephrase questions.
The system takes context from within the conversation to understand the user’s intent, creating a more conversational experience that is more natural and fluid. With natural language, users are able to interact with data as if it were a person. Users are only limited by the questions they ask, not by their knowledge of data analysis.
From Apple’s Siri to Amazon’s Alexa, there are a plethora of devices on the market today that uses natural language processing as a way for people to interact with their devices. This allows smartphone users to interact and query their devices using voice commands.
Integrating AI-powered chatbots in your existing BI systems helps users to swiftly access data without the hassle of logging into the system, and filtering dashboards. Business Intelligence chatbots simplify data consumption and interaction. Users can ask natural language questions and get crisp answers right to their actively used messaging app. And these responses can be delivered in various formats – text, pie chart, graph etc.
As data sources are continuously becoming more complex and numerous, data management has become even more important to BI systems. In today’s data-driven culture, data forms the basis of business decisions. Bad data will result in bad business decisions that can cause big problems for the organization. Data governance ensures that only certified people will have access to data. Making data more reliable, and business decisions more accurate.
In the past year, we have witnessed what the Wall Street Journal refers to as a “global data governance reckoning.” From Facebook to Aadhar to Marriot, high-profile data breaches have brought data governance to the forefront of the data management conversation.
On top of that, the European Union’s General Data Protection Regulation (GDPR) took effect, which left many organizations scrambling to become compliant. We can expect more countries over the world to try to implement similar data protection regulations. The need to create and implement data governance policies that protect data has become more apparent.
Smartphones are popular around the world with global sales hovering around 1.4 billion for 2018. In 2019 and beyond, we will see bigger developments in mobile BI, which allow users to access data at any time and from anywhere: airports, long train rides, etc. This speeds up the decision-making process which, in turn, improves the efficiency of the organization.
Mobile BI first made an appearance a few years ago – driven mostly by the popularity of mobile devices. The first iteration of mobile BI failed to make a big splash in the market. But in recent years, we have seen an increase in the number of companies that use mobile BI on a daily basis. There are a multitude of devices that exists in today’s society, and the way people consume information differs on each device.
BI vendors have a much better understanding of the mobile experience as well as the different devices and platforms that people use. This understanding will redefine the way vendors, and users, approach mobile BI. A number of BI vendors, including Microsoft Power BI, Tableau and Qlik, already offer mobile BI solutions.
AI has evolved into a technology that modern businesses cannot live without organizations use this technology to improve the efficiency of the business and their decision-making. BI systems are mostly used to analyse old data and past performances. Data analysts use this old data to create reports which managers use to make business decisions.
But this kind of reactive approach is slow and does not give a clear picture of current developments in consumer behaviour. Advances in AI have allowed organizations to use complex algorithms in order to gain real-time reports and make informed decisions. These advances have inspired a move towards a more proactive approach to analytics. In 2019 and beyond, we can expect to see more of these proactive analytics as companies seek to “predict” the future.
In order to compete in the market and stay relevant, organizations will turn to BI systems with AI and machine learning capabilities. The need for real-time reporting and user-friendly interfaces in the data-driven culture is becoming clearer. As business philosophies evolve and work to remain relevant, the way businesses use data analytics also evolves. There is a lot of pressure on BI vendors to evolve with the business world and change the way people experience data analytics. These trends that we have outlined will go a long way in helping the BI industry evolve in order to satisfy the needs of organizations of different levels.
Acuvate provides a range of Business Intelligence and Analytics solutions and services for the large and medium enterprise. We help companies in building a robust BI strategy, data integration and data warehousing services, setting up management reporting systems, machine learning and advanced analytics solutions, BI chatbots and much more! Everything we do is to help you make data work.