When it comes to belief in their data foundation and the insights it brings, most enterprises have a long way to go. Despite their investment in data initiatives, many companies still base their decisions on gut instinct. The reason for this is simple -- they don’t trust their data, their analytics, or both. The lack of trust is so widespread that, in 2020, PWC released a report that opened with the lines: “Your organization does not just need a data strategy. It needs a data trust strategy.” The problem, they summarized, is that “Data Trust isn’t just a moving target. It is many moving targets.”
The days when organizations could rely on third-party data are gone. Today, the only viable data strategy is to collect and analyze data yourself. But how do organizations go about collecting and creating data they can trust? The long answer begins with strict data governance, but there is a shorter version too. Here are a few ways to ensure trust in your data:
To gather data you can trust you need to plan. This means you need to know the following before you start:
Reliable data begins here, and it is an ongoing process.
Scrutiny and transparency are also key. Your data can’t be taken at face value. Instead, data needs to be monitored, validated, and cross-checked both manually and in real-time to give decision-makers the confidence to use it. That means three things:
Obviously, creating trust in your organizational data is a complex issue, but following the four Ts will take you a long way towards achieving complete, clean, and trusted data. According to the Five Ts, reliable data is:
This means data that is clean, complete, and consistent across all of your organization’s systems.
To be useful your data needs to be easily accessed and understood.
When it comes to organizational planning, data has a shelf life. Your data has to be up-to-date and available when you need it.
You can’t trust data if you don’t know where it came from or how it has been manipulated.
Have other users rated or reviewed the data using third-party data quality management tools like exMon?
“Scant benefit lies in having lots of information unless many people are using it. That
imperative has given rise to the “citizen data scientist.”
-- Isaac Sacolick, President and CIO at StarCIO
It’s simple, the more eyes that see your data the more insights it will yield.
At the 2018 Gartner Data and Analytics Summit Rita Sallam, the Conference Chair and VP of Gartner Research & Advisory, pointed out that not only is diversity the “right thing to do” when it comes to your hiring policies, it is critical when it comes to your data analysis and business performance.
Diversity cuts across the biases of age, gender, race, and culture. In addition, more eyes on your data bring factors like each individual on your team’s thought process, personalities, and abilities into play. The result is stronger data analysis and deeper insights.
Diversity, however, isn’t just limited to your team. There is a wealth of information in both structured and unstructured data. To their detriment, many organizations are setting aside unstructured data because they are simply unsure of how to put it to use.
“While there will always be live service, that type of service should be treated like a precious resource and reserved for opportunities that significantly move the dial on outcomes the company cares most about.”
-- Devin Poole, Senior Director, Gartner
The self-service model data has gone a long way towards democratizing data analytics. It is one of the key contributors to the rise of data citizenship. Unfortunately, self-service has its limits, and as the number of self-service channels and size of datasets continues to grow, so does the complexity. As complexity grows, channels become overloaded and BI units and IT find themselves swamped and often need to revert to manual processes.
So how do you scale self-service while proving more context and giving IT and BI departments time to respond? Gartner suggests that moving incrementally is the way forward. This means lower cost, lower risk initiatives with high strategic yields. Essentially, companies should place many small bets by providing simpler services that more, smaller, teams can handle.
Data literacy is the final piece of the puzzle. This means that every stakeholder in your organization needs to speak a common language when discussing your data. Going back to the idea of data citizenship, data literacy is not for a small, elite group. Humans weren’t born with an innate understanding of statistical analysis and patterns, but they can be taught.
Historically, the C-suite has used analysts to translate data trends. Today, however, seeing is believing. Data literate senior staff are in an ideal position to make better decisions faster, drive organizational change, and promote buy-in down the line.
Promoting from within and upskilling employees already familiar with your business is a win-win proposition. All stakeholders should have an opportunity to study data analytics. This includes both hard skills like data retrieval, statistics, and reporting and navigating technology, in addition to soft skills like communicating findings, recognizing patterns, and problem-solving.
This is pretty simple. Offer incentives for employees who upskill their data literacy skills to help customers and co-workers, or contribute to company growth.
Improving data literacy means data analytics is no longer the role of the IT or BI departments. That doesn’t mean that IT and BI teams have a reduced role, it just means they have greater support throughout the organization. When business employees can clearly share what they want to achieve with IT, the entire data culture benefits.
Create a pool of champions, power users, or mentors that employees can go to with their questions. You will be surprised how quickly their influence will make a difference.
These are just a few ways that organizations can adapt and improve their data management and data analytics strategies. As important and tightly interwoven as Data Literacy, Diversity, and Managing Complexity are, however, they are all and based on Trust. Without a culture of data trust, there is little point to the whole exercise.
Do you want to get the best out of your data? exMon is a data quality management platform and master & reference data management tool that ensures that your data is accurate, timely, and most importantly, trustworthy.
Find out more about how you can improve your data quality and establish trust with exMon.
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