Melissa AU Team | Data Quality | data governance, data quality tools, marketing operations

Change never comes easy. It’s no secret that in today’s information age, to be successful, a company must be data-driven. Data is not scarce today. At every touchpoint, we’re giving and receiving data. But, for many companies, putting this data to use isn’t as easy as it sounds.

According to an executive survey, 62% of the firms have invested more than $50 million in data and AI initiatives. However, before they can put this data to work, they must cross a number of hurdles ranging from legacy data environments and traditional cultures to growing data volumes and rapidly increasing demand.

So, what can organizations do to hasten their progress towards a data-driven culture?

Build Trust In Data

77% of IT Decision Makers don’t completely trust the data held by their organization. Some data may be inaccurate, some may be outdated, in some cases, the data may be formatted in such a way that it doesn’t conform to the other records. Just like all other assets, data is a tool. If you don’t trust it, you’re not going to want to use it.

Thus, the first step is to make data trustworthy. It is better to have 1000 verified records rather than 5000 unverified ones. Trust in data is directly proportional to data quality. Hence, efforts must be made to improve data quality at various levels. To qualify as high-quality data, data should be correct, complete, consistent, valid, timely and unique.

Conducting quality checks manually is next to impossible but there are a number of tools that can help. Data verification works by comparing data held by an organization with data in reliable third-party servers.

Let’s take the company mailing list for example. An email verification service will ping each email on the list to check whether it is in use and to ensure that it is held by the person named in the record.

These verification steps need to be conducted continually to maintain high-quality data. As the quality of data improves, decisions taken on the basis of this data will show better results and trust in the data will increase.

Make Data Accessible

Data is an asset that can be helpful to almost all departments within an organization. The product development team uses consumer data to assess where they need to focus while the marketing team uses it to strategize communication.

For the organization to be truly data-driven, all teams working on data must use the same data. Take a simple example. If the product development team is looking at data from city A but the marketing team is looking at city B data, the campaign is likely to fail. This means that all data must be brought together and made accessible to all.

Since you have data being collected from multiple touchpoints, there’s a high risk of partial or full duplicates being created. For example, the sales team’s records of a customer who shopped online may help bracket demographics but the customer service team may have additional information on the customer’s language preference. Move away from the siloed data structure and take data quality checks up to the next level by consolidating and completing records.

All of these data aspects must be brought together and combined to create a single, unique Golden record. If all departments take references from this data set, the organization will be largely on the same page. Thus, data-driven decisions become easier to make as well as understand. Minimizing duplicates also makes the data analysis results more authentic.

Treat Data As An Asset

There’s a tendency to overlook things that we get plenty of. As mentioned in the beginning data is not scarce and so may go by unvalued. For the company to become data-driven, this behaviour needs to change. Data must be treated as an asset with as much value as everything else. It is a product in itself. Appointing data managers and data officers can help bring about a sense of ownership. Where earlier roles such as Chief Data Officer were non-existent, today, many companies are working on formalized programs and articulating such positions.

Start Small

When you’re at the cusp of transformation, it is important to start small. Focus initial data programs on distinctive use cases or high impact business problems. By using data in such situations, you can demonstrate value and build credibility in the database. Working with data typically requires patience and time. Hence, using it to achieve specific goals helps demonstrate the impact it can have. On the other hand, investing in data capabilities and technology without clearly defined goals can be frustrating.

Persevere

Whether you’re talking of a startup that’s just building its own identity and culture or an established multinational with an existing culture, moving towards a data-driven identity takes time. Even if the results aren’t immediately visible, the organization must persevere and prove that they are in it for the long haul.

Invest in data and the tools needed to work with it as well as data literacy, data governance and awareness programs. Bring in people to the team who understand the value of data and can help teach others how to use it. It’s like baking a cake – you may have all the ingredients, but you still need someone to tell you how to put it together.

Final Thoughts

Data is a strategic asset that can propel businesses into the future. A survey of businesses facing the pandemic revealed that 72% of them felt they would have been better prepared to respond to the situation if they had better data insight. It also showed that 74% of organizations have started paying more attention to data quality after the pandemic. It’s never too late to start.

Treating data as an asset and improving the quality of data being used is instrumental in building a data-driven culture. This is a process that won’t happen overnight. But, as long as you are patient and persevere, the fruits of your labor will soon make themselves apparent.