Data governance is key to a data-driven business

Data governance isn't complicated, but its implementation requires expert advice, as well as local knowledge of the company and its peculiarities to develop a solution that brings real benefits.

Handshake amid abstract connections and data.
Metamorworks / Getty Images


One of the most critical assets of any organization is data. Nowadays, the amount of data organizations are generating is amazing. At the heart of this all is data governance. And while it’s a hot topic, despite all the articles and white papers devoted to data governance, it seems some leaders still don’t know what it is. We need to shed more light on this topic.

What is data governance?

A simple definition of data governance is the central processes and policies that govern the management of enterprise data assets. The main objective of data governance is to pinpoint what data and information are important, establish the processes to manage it, and measure how effective the effort in achieving business objectives is.

Data Governance is achieved via an established team consisting of focused technology and business stakeholders, which are to oversee data by documenting policies and controlling data is generated – capture, define, store, and distribute across the enterprise.

With the rise of big data and the proliferation of data sources, companies have more and more data at their disposal. Connected objects, smartphones, social networks and websites can collect a large amount of data on the performance of the company, for example, the customers’ feeling or competition.

In addition, analytics technologies are becoming more accessible and can analyze this data to improve business performance. These two phenomena gave birth to supposed data-driven companies.

What is a data-driven company?

A data-driven company simply refers to a company that constantly uses analytic technologies to analyze the data at its disposal to make decisions.

In a truly data-driven enterprise, all employees and executives use data in a natural and integrated way in their day-to-day tasks. Sales, marketing, and finance managers must be able to leverage on all the relevant data at their disposal to make decisions quickly and drive their businesses to success.

A good data-driven company can use the right data at the right time to make sound decisions. The strength of data-driven companies is also their ability to collect relevant data from all the different aspects of their business.

The benefits associated with this is that it allows them to better understand key elements such as customer behavior or market trends. Distributing data across the enterprise decreases the risk of making bad decisions, reduces the risk associated with enterprise data security, reduces efficiency, and decreases IT spend.

Why consider data governance?

The term "governance" obviously implies the notion of quality as it is the major purpose of data governance. Why should you consider data quality? The impact of poor data on your business is obvious: goods shipped to the wrong address, customers receiving products that do not match the description of the advertisement, etc. However, poor data quality can have more subtle effects: missing a discounted sale because you cannot accurately identify the product categories your customer buys, loss of sales on the Internet because your inaccurate sizing data discredit you on comparative sites, etc.

I guess you could also ask yourself, "Why do I have to make data governance formal in my business?" The reason is that it's likely that some of the company's employees already check the quality of the data as part of their day-to-day work. For example, it is likely that accountants will verify that the postings are recorded on the proper ledger accounts, your accountancy department verifies invoices sent and the corresponding payments are received.

Most of your operational data is already embedded in an active management process, but for the most part, the interest lies in not just quantities but qualities. Benchmark data is the one that is least subject to quality control as it drives many business processes. Data governance aims to establish formal management responsibilities for the quality of this data.

Data governance has caused a shift in attitude: we are moving away from a reactive approach to quality towards a more proactive approach. Often, we realize that the quality of the data is insufficient only when a process fails, when a delivery cannot be made or when the computer system stops working. In some cases, this is the best way to detect problems. It is also common for incidents to occur because of poor data quality that no one wants to take responsibility for! Data governance ensures that a person is clearly responsible, not only to correct problems, but also to reduce the risk of their occurrence.

A solid data governance strategy

More and more companies are looking to become data driven. Unfortunately, many fail to turn the data at their disposal into actionable information. The main causes of these failures are often the ambiguity around the project, and the lack of people, tools or technologies needed to achieve the set objectives.

For a business to become data-driven, several steps are essential. The company must first define what success metrics will be measured and relate these metrics to the datasets that will be used for their measurement. This initiative allows the firm to prepare and align the tactical execution of each department to the overall strategy of the company and to measure performance against defined goals and objectives.

Secondly, the use of data and analytical tools must spread throughout the enterprise. If not, the objectives will be more difficult to achieve. It is possible for leaders to drive adoption by quantifying and then sharing the financial benefits and impact on business productivity. As the use of analytical tools advances, adoption becomes more widespread and collaboration improves. In addition to these steps, several key elements are critical to the success of a data-driven business strategy.

These include:

  • A set of master data management (MDM) tools: a set of tools and methodologies for integrating and maintaining master data. These tools are closely linked to data governance and allow the company's data experts to maintain the master data domains for which they are responsible. It is important for companies to develop an MDM strategy in conjunction with their data governance strategy. Otherwise, the risk of failing the data governance program is magnified and the likelihood of ending up with incomplete and inaccurate information also increases.
  • Metadata management solutions to better understand data: Metadata management solutions help organizations understand data in a more holistic way by providing access to definitions and other data formulas. These tools include identifying database tables, reports, dashboards, and other components that might be affected by a database change. Thus, metadata management allows companies to analyze the impact of decisions before taking action.
  • Business intelligence tools to exploit data: Business Intelligence tools provide a better return on investment from enterprise data. With these tools, users can explore subsets of information, query, and develop predictive models.
  • An organized data architecture to transform data into information: To turn data into actionable information, organizations must adopt a tripartite data architecture. Each part must be designed and modeled to achieve specific goals. The first component is the "landing zone" within which the extracted data is arranged. The second so-called "compliance" component makes it possible to integrate this data. Finally, the "analytic" part makes it possible to transform data into formatted information so that it can be exploited by automated analytical tools and other Business Intelligence solutions.
  • Extraction tools to collect data: having a well-organized data architecture without data available is useless. It is therefore important for a data-driven company to acquire Data Acquisition tools, also known as extraction transformation load (ETL) tools. In addition, it is essential to develop a strategy to understand the changes made to the source system. A data change capture strategy helps maintain historical data over time.

Data governance is essential to enable decision-makers to have access to clear, relevant and consistent information. Data governance helps to ensure the quality of data and to define KPIs (key performance indicators).

Data governance is the key to a data-driven business because it not only saves money, but also helps companies make more money; ensures data consistency, reliability, and repeatability; solves analysis and reporting issues; guides all other analytics activities; improves confidence and provides clarity.

Many companies make the mistake of delegating the implementation of a data governance program to the IT departments, but these initiatives are more likely to be successful when they are directly supported by the leaders of the organization. For good reason, it is the managers who have access to company master data. Certainly, executives need the support of data experts to be able to deploy a reliable and robust data governance strategy.

Finally, data governance is not complicated. Its implementation requires expert advice, as well as local knowledge of the company and its peculiarities to develop a solution that works in your situation and brings real benefits.

This story, "Data governance is key to a data-driven business" was originally published by CIO.