Long recognized as a must in the data-driven world, data governance has never been easy for big and tiny organizations alike.
Today, the complexities associated with adopting data governance best practices are greater than ever.
Data, both structured and unstructured, is arriving in increasing volumes from proliferating sources to be stored and processed on multiple platforms. Points of integration are expanding, along with the number of regulatory mandates.
Even small companies have to deal with dozens of undocumented data sources and data silos, not to mention poor data quality or the horror of identifying flat files floating around the network with sensitive data inside.
In the background, the maturity of data governance initiatives has regressed. According to Quest’s regular survey of IT and line-of-business respondents, less than 15% of companies fully implemented a data governance program in 2020, which was half as many as reported in the previous research. Citing “understanding the right approach to data governance” as a major challenge, organizations are revisiting their strategies to check if they indeed follow best practices for data governance.
In this article, we will examine how leading companies are mastering best practices for data governance to eliminate costs across their data ecosystem and harness powerful data analytics solutions for new sources of revenue. Dive in.
Data governance is the practice of organizing processes, standards, and responsibilities to enable the company to know where its data is, how it is used, whether it is protected and how fully it meets data quality criteria (accuracy, completeness, reliability, relevance, and timeliness). It describes who can take what actions with what information, and when, under what circumstances, using what methods.
By implementing best practices for data governance, organizations reduce privacy and security risks, improve response to regulatory requirements, and enable more advanced data analytics and data science initiatives.
The goal of data governance is to manage data as a strategic asset.
To ensure the success of the data governance program, it’s also important to understand what data governance is not:
Following the adoption of the EU’s General Data Protection Regulation (GDPR) in 2016 and massive data breaches across sectors in 2018, security became a key motivator for organizations to adopt best practices for data governance. However, the focus has slightly shifted in recent years.
According to data governance professionals participating in a 2022 Zaloni survey, today’s investment in data governance is primarily driven by an effort to improve data quality (74%) and get faster insights from data analytics/BI (57%). In the 2022 State of Data Governance and Empowerment Report released by ESG and Quest Software, 41% of respondents ranked data quality as the top driver for applying data governance best practices against 37% who cited “improving data security.”
On the one hand, increasingly more organizations want to democratize access to quality data to drive better decisions.
On the other hand, advances in machine learning solutions have fueled interest in the revenue-generating potential of big data now successfully leveraged for business impacts. This trend is particularly strong among customer-focused companies that are struggling to get new insights as fast as possible to remain competitive.
Data governance is a pain. This should be accepted from the start. There is one big problem that makes data governance extremely hard. Above all, it requires a mindset shift. Similar to DevOps implementation, people and processes come first. Tools come last.
Introducing best practices for data governance takes a lot of effort because you need to instill rules and procedures around data access, consistency, and uptake of new data sources. This means holding a lot of meetings with stakeholders, rather than doing purely technical work.
The second biggest challenge is finding an optimum balance. When promoting best practices for data governance, you need to give business users the right level of flexibility that will be enough for exploring data but not enough for messing things up.
Regardless of how difficult, frustrating, or costly data governance is, it is something that any organization has to deal with eventually if it wants to become data driven.
Drawing on ITRex’s extensive data experience, we’ve collected a few useful tips on how to get started with data governance best practices. Jump in.
The first step would be to do an assessment of the organization’s data governance readiness to discover the pain points that are holding the company back. You need to prioritize them and build a high-level plan to work around priority use cases. Companies at the beginning of their data governance journey usually hire an external consultant to do this job (drop us a line, if you need an experienced data consultant).
For instance, you can even start building off from your CFO’s frustration over different financials they get for the same historic period every time they ask for them. Or the company’s painful problem can be an inefficient business process for discovering outdated transactions, or meaningless report generation, or siloed systems that lock important data away from users who need it quickly for forecasting patterns.
Identifying the quick wins of introducing data governance best practices and setting specific benchmarks will give you a compass and help you with the next step (see below).
Data governance has to start from the top to ensure role clarity and empowerment, so executive buy-in is key.
This part will be hard because data governance is rarely perceived as a profit center.
Just telling the C-suite that you want to improve data quality won’t be enough. You need a clearly defined business case built around the pain points identified in the first step. This case should promise value creation.
Basically, you should link your data governance best practices initiative to either revenue lost due to bad data and resulting business errors or man hours spent (e.g., by data teams finding, curating, or enabling data). You can also cite the costly risk of regulatory non-compliance or increasing spending on data storage.
Looking further ahead, you might also have a tough battle ahead of you fighting for buy-in from various departments. Your program might be labeled as red tape slowing down processes. Or you might have to deal with frustrated users demanding unrestricted access to everything that your policies won’t allow.
Getting buy-in from across the organization is critical to your data governance initiative. So, polish your storytelling skills.
Although each organization can set up its data governance structure differently, there are stakeholders that are most commonly involved:
A clear definition of roles and responsibilities is a fundamental part of best practices for data governance. If no one owns them, then no one will care about achieving the results.
Data governance is not a one-off project. It’s an ongoing program, and applying data governance best practices is a continuous, iterative process. This process involves complex technical activities, including:
To make the adoption of data governance best practices smooth and efficient, leading organizations also follow two strategies when designing their operating models:
The success of the data governance program is a company-wide responsibility. It ultimately depends on how end-users are data aware, literate — and excited about data enablement.
To make employees understand the value of quality data, leading organizations offer regular training. If a salesperson onboarding a client has to fill in dozens of table fields they don’t make sense of, why would they care about entering this data correctly? You need to educate users on the value of data governance best practices that is not always tangible to them. At the same time, you should also show how sticking to the rules can improve their personal and business results.
Data governance leaders also use various incentives. Such interventions include rewards for sticking to data quality standards or demonstrating new use cases, along with a punishment for errors uncovered in a periodic audit.
Set clear data governance processes and buy all users into them. Mastering data governance best practices is no easy fit, but your organization can succeed.
Also published here.