Organizations that aren’t data-driven stand to lose out significantly. Not only will they tend to exhibit a generally reduced understanding of clients and decreased competitiveness within the market in which they operate, but they also often suffer from a lack of skills and data culture that enables trust, transparency, and self-service business intelligence.

Together, this all adds up to missed opportunities, poor customer satisfaction, inefficient operations, and ultimately, increased costs and loss of revenue.

Recent research shows that more than 62% of companies haven’t successfully adopted a data-driven culture. Some of the key reasons why so many businesses fail at this transition include:

• Not investing enough time and money and treating data as a ‘one-time project’
• Lack of strategic alignment on a company scale
• No central repository that provides the holy grail ‘single source of truth’
• Overwhelmingly large volumes of data to manage
• Bad data (low-quality data)
• Unstructured data that isn’t legible (80% of all data is unstructured)
• Incorrect/ missing data
• Inadequate training
• No accountability measures
• Lack of visibility
• Not having the right tools in place

The list could go on. Common for most is that they have established a lack of trust in the data available. However, the good news is that most of these challenges can be addressed with effective data management principles.

We’re going to look at some of the principles and best practices of data management that will help you to drive a strong data culture and enable data to better support your business and decisions.

First things first, you need to create a data management strategy. This should be clear, coherent, and comprehensive, and it’s vital that all stakeholders are brought on board during the development process to ensure buy-in and follow-through.

To create this strategy, pose the following questions…

• What are your data goals?

Do you want to improve efficiency and therefore productivity, increase sales and your bottom line, or perhaps you want to upgrade your product/ service/ solution portfolio to elevate customer satisfaction? Whatever your goals, it’s essential that they are in line with and ultimately support the business’s overarching objectives.

• How are you going to collect, store and analyze data?

There are a whole host of techniques, tools, and software solutions to choose from that will make it quick and easy to harvest data. Think about the end user here, and ensure you invite ideas from all departments about how data will be gathered and analyzed.

• What parameters are you going to use?

Every piece of data from each department will now be under scrutiny. It’s time to look at what fields and characteristics each set of data need to provide. For example, sales data a logistics data will incur very different characteristics.

• Are you aligned on how you talk about data?

The first step is to ensure that everyone in the organization is on the same page regarding how they understand the terms when they talk about data. For example, what do you mean when you talk about products at the organization? When somebody from the innovation team talks about products, they’re not talking about the same level of products as somebody in the global supply chain, or in customer service teams. Think of it like your organization’s data dictionary – it’s critical that all departments read and understand it.

• How will you ensure quality?

Poor data quality or ‘bad’ data is useless to a business. All data should be monitored for accuracy, completeness, reliability, relevance, and timeliness. Will somebody perform this task manually (we strongly advise against this due to the time required and risk of human error) or will you implement a tool that will continuously monitor your data?

• Have you thought about data security?

Data breaches happen on a regular basis, in fact, more than 4100 cases were publicly disclosed in 2022 (and that’s just the known ones!) It’s essential that businesses protect confidential business data as well as sensitive customer data. This is also where data governance comes into play – these are the practices that ensure data is handled and processed correctly – you can learn more about data governance, here.

• What resources, processes, and technology are you going to use to achieve your goals?

Who is going to implement this plan, how much time are they going to dedicate to it and how much money can you invest to get it right? These are all difficult questions that must be answered to foster realistic expectations. From there, you can look at the processes that need to be created and followed, and what technology should be put in place to make the whole job easier.

The role of a data management strategy is to underpin your whole data operation and act as the building block to achieving your overall business goals.

At the very core of every business that practices good data management, is accountability, also known as data ownership.

In the world of data, this simply means not offloading all data responsibilities to your IT, data, or BI team. Think about it, it’s impossible for your data team to take ownership of every single piece of data within your business. Their job is to monitor aspects such as regulatory compliance, governance, and data integrations, not to update customer data – that task should probably be down to Sally from sales, to update customer data based on a recent meeting.

Instead, each department should be able to take full responsibility for its own data, and ensure that it remains in a state in which it can be shared easily and add value for other team members or departments. As we mentioned earlier, to guarantee value, data should be accurate, complete, reliable, relative, and timely. Sales should be in charge of sales data, the finance of account data, and so on. Team heads or managers should assign data owners that could be based on the data that an employee naturally already handles. While data ownership may sound scary for some of you, there are loads of tools and platforms ready to help. The important thing here is to ensure that the BI department does not become a bottleneck. When data issues are not related to integration issues or other technical challenges the accountability to ensure the quality of that data should go straight to the data owner. 

Alternatively, you could also think about assigning a data steward. This is the person who oversees the quality and is appointed to fix a specific data quality issue. 

However, one important thing to remember is that while departments should of course own their data, it’s critical that teams don’t work in silos. Data should be easily and quickly accessible to anyone who may need it – an effective data management platform with administrative profiles and controls can make this is reality. 

Data is great but unless it’s in a condition where it’s useful, it’s pretty much useless. This is where data quality comes in, and why it’s so important.

Data quality is absolutely crucial to evoking trust. If data is collected and it’s not accurate, or it’s not complete, or it’s not up to date, or there’s something else wrong with it, making it bad data, it’s not trustworthy. If your employees or teams can’t trust the data to accurately inform critical business decisions, then they simply won’t use it. On the other hand, if they do trust poor-quality data, they’re at risk of making decisions based on inaccurate data, which is an even bigger problem for the business and can have catastrophic consequences.

While data quality standards may differ, based on factors such as what the data is, what it will be used for, and for which department it’s primarily used, there are guidelines that should be followed to ensure quality doesn’t slip.

Manage the data lifecycle – this is the sequence of events that data goes through in its lifetime, and includes its creation to its destruction and everything in between

Is the data up to date? Outdated data is not useful for anybody, as soon as data parameters change, it should be up to the data owner to rectify it

Is the data accurate? Incorrect data can wreak havoc on businesses, data should always be checked and monitored

Are any fields missing? Incomplete data is also a big data quality issue, important fields may be vacant, without which, anyone who views that data may not be able to see the full picture of that data set

Is the data structured? Data that isn’t structured means somebody has to manually go into that data and structure it so that it’s easily readable

By always asking these types of questions, or adopting a solution that guarantees these characteristics are taken care of for each and every data set – and flags issues when it can’t – businesses can truly trust their data and use it to make critical business decisions without worrying about bad data.

Businesses can make navigating datasets easier by using metadata. Metadata is essentially the breadcrumbs that lead to datasets and lets employees not only locate data but also see things like:

• If any changes have been made to that data and when
• Where the data was collected and how it was gathered
• If that data has been analyzed and when
• Documented workflows that show the process of storing, tracking, and editing data
• Any areas of concern/ anomalies that should be addressed 

Metadata can be broken down into three categories: descriptive, administrative, and structural. Descriptive metadata looks at how data is described, i.e. its properties such as who owns it, where it’s been shared, what it actually contains, etc. Administrative data concerns when it was created, when it was edited, and who has access to it. Structural data considers how data is organized, how it’s categorized, and if it relates to any other data sets in the system.  Which type of metadata you need will depend on what the data is used for and who needs access to it. 

By having this level of information, businesses can then look into any potential areas of concern within the data itself, significantly speeding up the rectification process. It’s key that organizations develop a clear approach to metadata that aligns with the overarching data management strategy to support business objectives.

All of the above principles support effective data management and increased data use. If you have gone to the trouble of following these steps and best practices, then you can also maximize the value of that data by using it more effectively.

What do we mean by this? Well, there are a few things you can put in motion to maximize the value of your data. First, you can share access to data across all departments, as one set of data can inform many business areas. Second, you can simplify usability, and make it easy for end users to put data to good use, i.e. better inform actionable insights. Third, conduct regular reporting and data analytics, so you can clearly see what’s being used, what’s not, and why.

By doing this, businesses can see what data is useful and is actively being used, and what data is simply taking up valuable server storage, time, and resources. Remember, data should always be useful, if it’s not useful, leave it behind.

Data management is a challenge for many businesses, but it doesn’t have to be. By following industry best practices, you can make data management a breeze for your business. 

If you’re looking for a solution that takes the hassle out of data management for you, then take a look at the exMon data management solution. Not only can it install and monitor rigid data quality standards, but it’s also easy to use and simply overlays your existing data platform. Learn more about exMon’s data management solution, here, or reach out to an expert to book a personal demonstration.