Data management best practices for business

Big data is evolving at such an incredible rate that organisations are struggling to keep on top of their internal and external storage silos. This is, quite rightly, leading to ‘big’ concerns over data management best practices.

Companies collect data from many different sources, and each dataset will be regularly competing for analytic importance. This can cause a system to become overextended, and only a small number of businesses believe that they’re getting the most productive use out of their data.

Enforcing a practical data management solution will not only improve the quality of the data you collect, but can also be the first step towards solving data productivity issues. Providing relevant, substantially analysed and timely offered data can also allow your business to make improved decisions regarding its operations, ensuring it grows successfully.

Implementing data management solutions into your business can be quite a task, and if you’re yet to begin the process, you may be at a complete loss as to what data management actually entails. In simple terms, it is the practice and principles put in place to allow for the successful management and maintenance of all data resources stored by a business.

Accompanied by a data management strategy that’s designed to suit the specific needs of your business, each newly created data asset will undergo detailed monitoring procedures through its entire lifecycle to ensure it is fit for purpose and kept safe from security threats. Here are eight data management principles and practices to help your business reach the top of the big data ladder – from the experts shaping the industry.

Select your business goal before the data

The volume of data collected in business environments is expected to snowball over the next decade, with the continuous addition of digital devices to networks and systems. This constant flow relegates previously collected data further down the storage silos as new data takes precedence.

It’s common practice to use the data to identify and meet business goals. However, data scientist Lillian Pierson advises that you refer to your business goals throughout the planning process to determine which datasets hold the most important information and whether or not they need to be placed in a storage silo.

“It sounds obvious, but make sure to start each initiative by clarifying your organisation’s goal for that effort,” says Lillian. “Use that target as the main criteria for deciding which data to keep.”

Pierson also suggests you consider how each dataset may impact the core KPI that you’re looking to improve. “You need to decide what data to store based on your goal, not the other way around. By this I mean that many companies keep way too much data — data they have no clear purpose for storing.

Lillian’s advice? Always start with your goal and then decide what data and data technologies you need to achieve that goal.

Look to the future with machine learning and artificial intelligence

As a business begins to accrue more datasets, the time it takes to analyse and report on each of them increases. Implementing new techniques like AI can lead to the deeper level of extraction in the collected datasets, with so much to gain from allowing machines to aid in analysis. Fiona Salmon, UK Managing Director of predictive data company 1plusX, champions the idea of the collaboration between the technologies, particularly in regards to the impending GDPR legislation.

“Because of ‘big data’ and the 3 Vs, AI is becoming essential to businesses. The amount of information businesses now have to process means it’s humanly impossible to interpret and extract intelligence from all of that data in a way that is economical, as fast or as high quality as AI can deliver.”

Salmon believes that new General Data Protection Regulation (GDPR) means many businesses collecting large amounts of customer data are likely to look to AI to help them comply with the new laws.

The GDPR requires companies collecting data to enable consumers to opt-in and out of communications easily, provide reports to consumers on what data is collected about them, and provide easy ways to delete that data. Without AI tech, that will be time-consuming and costly for businesses.

“Incorporating AI into the analysis and management of data will add the equivalent of millions of data scientist man-hours to a data manager. Machines will do the boring tasks while the data manager can focus on more-human and creative skills, making their work more enjoyable.”

Put the right people in charge of your data

As a business, putting the proper practices and principles in place should help to accomplish a definitive data management strategy. But what you need to remember is that success also comes down to putting the right people in charge of your data management, and this is a process recommended by Mark McGuire, Director of Data Managed Services at Morrison Data Services.

“Our people are our best asset when it comes to managing data,” says Mark.

“Early in the noughties, if we were seeking to undertake business analysis we typically used IT teams, who had excellent coding skills and would generally work to a specification provided. Our approach now is to channel that expertise within the business unit, where responsibility for delivery and control are aligned.

“Equally, we encourage engagement with internal and external customers, to gain a better insight of requirements and to allow new ideas to spawn. Data Security is essential and always has been, but clearly, GDPR is placing a new level of scrutiny in that area.”

Implement data governance practices

Gaining significant value from data is a critical aspect that a business should incorporate into a data management strategy, and one of the first steps to achieving this is to introduce data governance practices as this will ensure the data used by a business is of a high quality throughout its entire lifecycle.

Centering on the availability of data, usability, integration, and security, data governance is an evolutionary process for new businesses that incorporate data management practices, as it allows for the entire industry to use the data.

Colin Lye, a Practice Lead of Big data and Analytics at Northdoor believes that a governance framework spanning business terms through to metadata, including both structured and unstructured data, will provide the bedrock for all information architecture initiatives.

“You’ll reduce costs, improve security, and provide compliance, data quality, and meaningful insight. You really need to implement an enterprise-wide, useable governance framework to reduce your operational costs, de-risk subsequent projects, and provide the foundation for business-disrupting insight.”

Ensure your data is easily accessible

Whether you’re an SME or a Fortune 500 company, it’s important to store your data securely. But what’s also crucial is to make sure the data isn’t forgotten, as this will ultimately make it defunct. When storing data, it is essential to provide ease-of-access for those who need to use it, while also negating access to those who do not have the correct clearance.

To cope with swift progression into the digital age, it’s key to keep on top of your data access protocols.

“Organisations looking to manage their data storage better should be sure that wherever their data is stored, it is easily accessible to the relevant groups in the business,” says Sam Underwood, VP of Business Strategy at data analytics consultancy Futurety.

“As we continue to move towards a more data-driven culture, it’s important that your organisation is well-prepared to pull data into dashboards or other visualizations to guide future strategies.”

The message? Data kept in a silo is of no use to your company.

Plan against cybersecurity threats

If you suspect a data breach, one of the biggest mistakes a company can make is to deviate from their Incident Response Plan. By having a plan with clear decision points, companies will know if they are required by law, regulation, or good faith to disclose a potential or realised breach.

“In a pre-established Incident Response Plan, your company should also have decision points driven by legal counsel and company leadership that determines if third-party Incident Response services need to be engaged to help contain, eradicate, recover, provide expert witness testimony, or help with the clean-up,” CEO and President of PhoenixNap Global IT Services, Ian McClarty, advises.

Develop a holistic approach to data management

The upkeep of data management practices and principles is a task that should be undertaken by your entire business, not just the team that is required to monitor them. Employing a holistic approach to this process allows all members of the company to share data infrastructure and paves the way for new and efficient management processes.

Cristian Rennella, CEO of, illustrates how partnered with strong governance, this technique can pave the way for successful master data management.

“On many occasions, a data management team ends up working in a separate area of the organisation. However, for the company to be successful, this area must be integrated within the company and for this to succeed it is necessary first to define the policies and culture of a business.”

From this, data can be studied from the correct perspective to reach conclusions that align with the objectives of a business.

Manage classification, ownership and keep data clean

Introducing data classification and ownership should be a straightforward concept to launch into a business environment. However, organisations have been overlooking these processes, and if you produce vast amounts of data, it is vital to stay on top of these practices so that any arising issues are solved.

“We have lots of data, so it’s key that the data is classified, and that someone within the organisation is made accountable for it,” says Michael Blakely, Managing Consultant at Data Protection Consultancy Pangolin. 

“It’s becoming especially important with GDPR coming into place that someone is responsible for this. The business also implements strong data management, and retention policies, which ensures that we are aware of the data flows through the organisation, and we are not over collecting. Keeping the data, we collect lean ensures we don’t end up with over bloated servers and spiralling costs.”

To ensure you follow the best practices available, he believes that companies should move beyond just clarification and ownership and ensure you have the best data authenticity and verification across the life cycle of information flow.

“Make sure you get sufficient board and management buy-in at the start otherwise you will be fighting an uphill task for the duration of the project,” he adds.

Following these recommended practices and principles should help you to implement an affirmative data management strategy in your business, which will lead to better productivity and significant potential for growth.

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data management practice and principles


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