Business intelligence combines the strategies and technologies used by enterprises for the data analysis of business information including online analytical processing, analytics, data mining, business performance management, benchmarking, predictive analytics and other.
Every successful company is looking forward to establishing a strong business intelligence (BI) strategy, which combines historical BI with forward-looking predictive analytics. To provide successful BI strategy, companies need to create a strong BI team which can effectively organize their tasks and follows BI plan. The BI team should include developers, data/business analyst, IT developers and data scientist, who use the information to supply the company with reports, analytics, create database systems and applications, and manage metadata. Tableau, Qlik, and Power BI are self-service analytical tools that enable business users to perform reporting and analytics on their own with little or no support at all, from the IT organization. There are several factors that have influenced this trend:
- Companies are overcrowded with big data and IT sectors can’t deal with it easily
- Business users can create their reports directly using business intelligence
- IT companies analytical projects can take months, while a business needs this information in weeks
IT Business intelligence teams are spending most of their time on gathering information, cleaning, and structuring data for the business to create their own analytics through self-service tools. In other cases, they provide industrial strength reporting that can scale to thousands of users where secured verified data is required. The main question is, what companies should do to make IT business intelligence team more valuable? They should keep in mind that having a strong IT business intelligence team means more success as it can create a powerful predictive analysis. We are going to show you 4 easy steps on how to make IT business intelligence team more valuable and successful. Try to apply one of these steps that have proved to be helpful in large enterprises.
1. Create a centralized team to provide a data science capability
IT business intelligence team have to analyze data coming in from many different sources, as well as many different information such as core data (data generated by business via mobile applications, website, online shop), peripheral data (data generated from purchased products or services) and external data (data gathered from things like sentiment analysis). Data scientists are using computer programming, statistics, analytical tools, and machine learning to pull out actionable insights from big data. However, most groups within a company can’t justify the cost of a full-time data scientist, but by adding him to IT BI team, companies can create a centralized team that can provide precise and predictive analytics. This team is creating analytical data structures and reports with the ability to derive insights from the data.
2. Add decision architecture methodology
The decision architecture methodology can help companies to make practical use of information flooding their desktop. This methodology is providing a framework to translate the business problem into hypotheses, questions, decisions, and actions, with data needed to build analysis. It is also used for capturing analytical requirements focused on the questions asked of the data to surmise Dimensions and Facts. The decision architecture methodology has a deeper focus on decisions that the business makes and enable actionable insights.
3. Add decision theory to analytics
Decision theory, along with behavioral economics, represents an interdisciplinary approach to determine how decisions are made given unknown variables and focus on understanding the components of the decision process to help IT BI team create an analysis that guides to the correct choices. Decision theory combines psychology, statistics, mathematics, and information science to analyze the decision-making process and explain why we make the choices we do. This approach includes many tools such as thresholds, alerts, decision matrixes.
4. Establish a report certification process
Creating a standard for health and validity of a report will be important if the analytics are to be consumed by a broader audience, as report proliferation occurs with self-service analytics. Suppose we have one group to create a report with key metrics sourced from uncertified data sources. By putting reports through a certification process similar to UL labeling on electrical products, many consumers will know the level of scrutiny that has been put on the report by Data Governance and IT teams and trust the data contained within it.
Try to apply these steps to your IT business intelligence team because it is responsible for the decisions you may make in the future. This team is working with end users to define business requirements and needs, so make sure you are taking seriously these steps to improve your team.