Business Intelligence

Organisations continue to encounter significant challenges in how to extract maximum value from their data. To be able to generate meaningful insights to support decision making, data quality remains the biggest hurdle that companies must overcome. Reliance on outdated legacy data and technology infrastructures, multiple versions of the truth, along with limited traceability to source systems are the other most common challenges that make it difficult for organisations to maintain accurate and quality data within their ecosystem.

From a strategic perspective, organisations often struggle to mature their data and analytics function. The lack of clarity in defining the data and analytics services that their functions need to support adds to the existing challenge in identifying and developing the data analytics skills that their teams need. Owing to the limited expertise, organisations often battle with strategic challenges such as innovating in how to best use data and analytics, benchmarking current capabilities with the future desired state and selecting tools and technologies for end users.

Reports continue to be produced manually, largely using spreadsheets, with limited to no use of cutting edge techniques such as automation and visualisation. This is not only time-consuming but poses a high risk of human errors. Despite technological advancements in recent years, companies still struggle to consolidate all the customer data points they have across multiple systems into a single source of truth. Obtaining a single view of the customer may not be feasible even today for organisations including for example banks and insurance companies.

How can organisations overcome these challenges to generate increased value from their data? There are a number of key areas where organisations can focus their efforts to begin to unlock the true potential of the vast data repositories they hold. Having strong fundamentals related to information strategy, data management and business intelligence are a good starting point and can underpin any organisation’s unique data and analytics requirements.

Implementing a sound information strategy can help organisations to agree priorities for their data. Questions that organisations need to answer include what capabilities does the function require, what skills and resources are required, what is the technology roadmap for the data and analytics function, how mature is the function and what is the optimum future state, and what new technologies are required to support business needs. Once an organisation understands its goals and priorities, understanding how to organise and structure the data and analytics function is key.

Data Management refers to the suite of processes and technologies that allow companies to collect, store, use, and share data and information. Key considerations involve understanding how data is stored and managed; what technologies are used to manage data and how they interact; how to extract, transform and load data; whether the right structures and governance is in place to manage data quality; how to migrate data from one source system to another; how to build data warehouses, data marts and data lakes; and how to manage data security and privacy. Key areas where data management can benefit organisations include integrating data from multiple systems, extracting and processing data, implementing data quality measures, building a data governance framework, and managing data ownership.

Business intelligence (BI) combines business analytics, data analysis, data visualisation, data tools and infrastructure, and best practice to help organisations to make more data-driven decisions. Key considerations involve understanding how data is used to create operational and strategic report; the level of automation in reporting processes; whether visualisation is being used to generate insights in data; whether infographics are being used to represent complex data; what challenges typically faced when accessing information for decision making; and can slicing and dicing functionality be added to reports to allow for deeper analysis.

Areas where BI can add value include in the design and implementation of dashboards that can be used to generate insights for decision making. Dashboards can be built in such a way that data is refreshed automatically in real-time, ensuring that insights are available at your fingertips. Dashboards can also offer self-service capability so that end users can generate their own insights rather than having to call on data experts to build and run ad-hoc queries.

In order to drive continued value from data, organisations will have to focus on their data and analytics strategy. Some areas, for example BI, will be easier to implement whereas others will involve greater investment and commitment from senior business leaders. Data will continue to underpin how organisations increase their profitability and remain relevant to customers and hence it is critical that it is given sufficient priority across the business.

Business Intelligence