Critical factors to fuel the data-driven enterprise

Successful digital businesses depend on data to provide the insights necessary to deliver new innovations, products, and services. But those insights are only as good as the underlying data. As the number of business decision-makers who depend on real-time data increases, the issue of data governance grows increasingly important.
Data governance is the overall management of the availability, usability, integrity and security of all data used in an enterprise. A good data governance plan will include the existence of a governing body, a defined set of procedures and a plan to execute these procedures.
This is according to Anton Jacobsz, managing director at Networks Unlimited Africa, in a comment on an article written by John Walton from Information Management. The article notes that, “The term ‘data rich, information poor’ refers to organisations that have not established the critical components needed to transform their data into actionable insights.… To become data-driven, organisations must provide decision-makers with timely access to clean, consistent, reliable and actionable information, while avoiding the myriad challenges that lie in the way.”
Commenting on the article, Jacobsz clarifies, “In 2011, Sir Tim Berners-Lee, who is generally regarded as being the inventor of the World Wide Web, commented that data was the ‘new raw material of the 21st century’, likening it to the significance, in the past, of raw commodities such as iron, aluminium and petroleum products, which are all used to produce various processed manufactured goods.
“This interesting comment from Sir Berners-Lee sums up the true importance of data in today’s world, in which we value data for its ability to present insights that in turn help to guide business decision-makers. The right data at the right time can present critical deciding factors that can leap-frog a business ahead of its rivals. However, as we know, the quality of our output begins with the quality of our input. This is where data governance comes into its own: it is key to delivering consistent and reliable information.”
The author sets out eight critical elements that underpin a successful, long-term information management strategy, as follows:
Governance
Any enterprise information management initiative must blend strong C-Suite sponsorship with representatives from all areas of the business to create a blended team of clinical, IT, and business stakeholders.

Data governance
In order to make accurate business decisions, it’s essential that leadership has access to clean, consistent, and reliable information.
Master data management (MDM)
MDM is closely intertwined with data governance, and is a set of tools and associated methodologies that foster the integration and maintenance of master data. Data stewards must be able to easily maintain the master data domains for which they are responsible.
Metadata management
Metadata management helps companies develop a more holistic understanding of their data by providing easy access to data definitions, formulas, and other essential details. These solutions also facilitate data lineage, which provides a visual depiction of the data’s origin and any changes that occurred prior to it appearing on an executive dashboard or report, which works to create a high degree of data transparency.
Business intelligence (BI)
BI tools enable users to drill down into subsets of information, conduct ad hoc queries, run predictive modelling, and a variety of other functionalities essential to becoming truly data-driven. A key challenge hindering more widespread BI adoption is that many organisations have a poor information management platform, also known as data architecture.
Data architecture
The journey from raw data into actionable analytics is a complicated process. To be successful, companies should adopt a three-tiered data architecture approach in which each layer is designed and modelled to meet specific objectives, namely a “landing area”, a “conformance layer” and finally an “analytic layer,” in which data is transformed into a usable format for BI initiatives.
Data acquisition
In order to populate the data architecture, companies need access to data acquisition tools, also known as Extraction/Transformation/Load, or ETL, tools. Additionally, it is necessary to develop a comprehensive strategy to detect changes in the source system data.
Technical architecture
In order to meet the demand for high performance and reliability, a robust technical architecture composed of dedicated, properly configured servers is required.
As stated in the article, attempts by business owners to become data-driven are often handicapped by ambiguity surrounding the project. Another factor that hampers the move to a data-driven enterprise is a lack of awareness and understanding of the people, processes and technology considerations that are required to make these initiatives successful.
“A data governance strategy makes sure that data is handled in a proper and compliant manner across the business, and is the foundation of effective data management. Data management sets out to ensure that an organisation has different types of data stored in the right place, and that it is being used for the correct reasons. Data management oversees the discarding of unnecessary data, and that data flows through the business cos-effectively and efficiently. In South Africa, the looming implementation of the Protection of Personal Information Act (POPIA) reminds us of the importance of data governance,” concludes Jacobsz.