It cannot be overstated enough how essential data is for the business of today. In fact, a 2023 Salesforce study revealed that 80% of business leaders believe that data is a critical component of making decisions and 73% say that data helps reduce uncertainty and helps them make more accurate decisions. Thus, it is important that the data the businesses have access to is of good quality.
But what makes data good in the first place?
Good data vs bad data
Data is considered good if it provides complete, consistent, relevant, and accurate information as needed by the end-user, organized in a logical manner. It adds context so users can extract meaningful insights. It also provides timely and trustworthy information that empowers leaders to make smart decisions.
On the other hand, bad data provides information that is inaccurate, incomplete, and inconsistent, to the needs of the end-user. It is also unusable because it is disorganized to the level that users struggle to find the relevant information they need.
Bad data is not only a pain point for those trying to find particular information. If left unaddressed, it can lead to forecasting errors and operational inefficiencies. In worse cases, it can be the cause to security risks. In more serious cases, bad data can cost millions for businesses from which they may not be able to recover from.
How to prevent getting bad data
While the qualities of good and bad data seem to be straightforward and not difficult to distinguish, the reality is that many organizations find it difficult to distinguish good from bad data, primarily because they get overwhelmed by the amount of data they have accumulated and continue to accumulate.
Thus, in order to ensure that they are not getting bad data, organizations must establish quality control on their data early on, or at least as soon as they identify potential problems with their data. This may be a daunting task for some enterprises but it is not impossible to do so by accomplishing the following tasks:
Create data governance guidelines - Have a system in place that determines who owns data and who is responsible for it. It is also important to establish clear definitions of what good data looks like for the organization. This will help lessen the complexity related to data and make it more manageable.
Conduct data audits: Review the data consistently on a regular schedule to address bad data early on and address it promptly before they can negatively impact data quality. It’s also important that the findings of these regular audits are shared throughout the organization so everyone can stay on the same page regarding improved or decreased data quality.
Align data strategy with objectives – It is important that the data the organization is handling will help further its goals so the data strategy should reflect these goals. To drive impactful discoveries, it is helpful to identify key analytics programs that directly support these business priorities.
Use flexible tools – Use tools that will enable the integration of data sources of the organization such as asset inventories, system usage logs, and business metrics to have a more comprehensive view of the data. Leveraging solutions such as to efficiently clean, structure, and analyze large, complex data. When deployed appropriately, these tools should be able to present clear, simplified outcomes and visibility tailored to each stakeholder's needs.
Break down silos – While data silos have their purpose, they can hinder accessibility to data. Furthermore, quality of data is further enhanced by collaboration, and in turn, collaboration brings about data democratization, providing not only a unified view of data across the organization but also provides greater context to it so informed decisions can be made.
With the right strategy, technology, and leadership, organizations can gain control of data to the point that they can ensure that they get good data that can be presented to their stakeholders. Organizations can tame data chaos into an asset that drives competitive advantage.