Data analysts are facing data quality challenges around the globe today. Good data quality is crucial for deriving the right insights from the data available and making the right decisions.
1. Business-Wide Automation
Use your data to isolate the right KPIs, track results, and see meaningful outcomes. For those being done, automation is necessary.
In 2020, automation set to take the BI spotlight include Artificial Intelligence (AI) and Machine Learning (ML)
These two solutions are changing the way we deal with data management and handle business analytics. 37% of companies have implemented some form of AI at the end of 2019 and many others are on their way to developing projects for deployment, which can make crucial processes easy, as well as provide data from novel channels.
2. Data Governance
Since business data grows, data governance is coming to the front position as a pressing business intelligence trend. Companies have to find the ideal balance between access to their data on a company-large scale, and their wide security efforts.
This issue specifically is predominant in 2020, taking into account the number of high-profile data breaches that occurred in the last few years and recent amendments to the regulations of data protection.
The solution delivered by BI tools to data governance is centralization and ‘permission control’. A good BI tool makes the issue of data governance much simpler by just centralizing where all of the reporting functions are located.
3. Data Quality Management
As the amount of data, handled by the company, never cease to grow, data quality is becoming more and more of a BI priority. The outcomes received out of data are as good as the data you fill in your system.
With advanced data processes, businesses can better determine the consumer data they need to focus on for actionable outcomes. AI and IoT are particularly useful in such implementations since they provide an automatic focus on the important analytics, which allows you to achieve quality data not sifting through limitless data outcomes.
4. Predictive and Prescriptive Analytics Tools
Big data is becoming the central focus of all analytic processes. Both big and small companies are benefiting from leveraging these processes throughout 2020.
Predictive analytics
Predictive analytics involves extracting information from already existing data in order to predict future forecasts and opportunities. The predictive analysis may be utilized in multiple ways, including the prediction of forecasts surrounding customer numbers, or future purchases.
Prescriptive analysis
The prescriptive analysis is the process of the usage of the predictive results in order to settle on KPIs, which companies can work towards to achieve that predicted future. Prescriptive practices can help companies give customers what they want by using data visualization and graph analysis methods.
+1. Action-orientated Reporting
A lot of business intelligence tools have forwarded the capabilities of prescriptive analysis a step ahead. Instead of simply providing insights on what should be done, they have created platforms that aid communication and enable the setting and tracking of KPI targets, which are informed by business intelligence analytics. The data is as much of a value as the steps taken in the light of what one has learned.
What usually defines an action-orientated BI platform is the presence of KPI reporting dashboards, which allows setting precise targets in a united system. Those KPIs can be shared with different people using advanced permissions systems, and then tracked in the same interface. This makes the outcome of actions clear and decreases the risk of miscommunication.
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