Common Mistakes Businesses Make in Data Management (And A Step-by-step Guide on How to Solve Them)

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Common Mistakes Businesses Make in Data Management (And A Step-by-step Guide on How to Solve Them)

Spurred by the pandemic, more and more businesses are employing data-driven strategies to drive growth and competitiveness in the market. While this is the right trajectory that businesses are heading towards, the rate that businesses are making good use of their customers’ data to guide business decisions is still very low. 

This guide serves to highlight the common mistakes businesses make in data management as well as offer you the step-by-step solutions to solving them. Whether you are new to data management in business, or you are a business owner or data steward, you will learn something that can help you improve the data workflow of your company.

What is Data Management and Why Is It Important?

Data management, sometimes also known as data stewardship, can be understood as a collection of processes and tools employed to help organizations manage their data. It typically includes the following processes:

  • Data collection – the gathering of specific information from specific groups of target audience.
  • Data access – the processes and strategies of making gathered data accessible to the organization to perform tasks including updating and organizing the data.
  • Data storage – the process and strategies of storing data.
  • Data availability – backup and recovery strategies implemented to ensure continuity of data during disasters.
  • Data quality – the processes to ensure data is clean, valid, and accurate.
  • Data security – the processes to protect sensitive and confidential information of an organization.

 

Data management should be treated as the utmost priority of each company, because good data management entails organized, accessible and high quality data of a company, which allows guided decisions to be made at all times. With that said, let us investigate the top mistakes businesses make when it comes to data management and how to overcome them.

Mistake #1: Negligence of Business Objectives

The first and greatest mistake that businesses often make is the negligence of the business objectives or the purposes of the data they are gathering. Most businesses know the ways to collect data, but the raw data collected from different occasions and at different times quickly becomes useless if businesses have no idea on how to leverage them and turn them into useful insights. Some businesses understand the importance of setting objectives prior to data collection; however, they fall victim to short-term business Return of Investment (ROI) and by doing so, turning the data they collected obsolete and inapplicable to offer long-term value to the business.

Hence, it is important to first define your business objectives before jumping straight into data collection. What are the problems your business is trying to solve? What are the sales and marketing goals your business would like to achieve in the long run? 

Once you define your business goals, you can then proceed to nail down the objectives of data collection. Are you collecting data to better understand your customers’ buying habits and create a buyer profile for your business? Are you collecting data to train your sales and marketing teams? Or are you collecting data for an email marketing campaign you are doing? Defining your data needs and goals will help you better strategize the steps towards achieving your business goals, as well as direct your data management and sourcing processes, so you do not end up with tons of irrelevant data to your company’s needs.

Mistake #2: Ignoring Data Quality

While laying down the framework of your business is important to facilitate effective data collection and management, the actual quality of the data is also crucial. Data needs to be of high quality, that is, accurate, relevant, complete, reliable and consistent, to ensure the effectiveness of data management systems, and adequately facilitate business goals and decision-making. 

The problem of many businesses is that they treat data quality as something of trivial importance, and it is all too late when the quality of their data becomes questionable, and business decisions have been made based on inaccurate and unreliable data. To prevent this from happening, the first step you can take is to invest in a dedicated team responsible for upholding the data quality and hygiene of your organization.

After designating a dedicated team for your company’s data quality control, the next step is to employ adequate steps and tools in maintaining data quality. Performing data cleaning on a regular basis is one of the most straightforward ways to maintain data quality. Data cleaning is the process of “fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset”, to ensure high data quality. On top of that, data should also be regularly checked for accuracy and outdated data should be removed. Outdated and stale data do more harm than good to your business, as they can negatively impact your analytics and other efforts in sales and marketing. 

Additionally, as an extra preemptive step, businesses can invest in training their team members who have access to the data on the proper ways to collect and input data. Most data input processes nowadays are automated, but it is worth considering training your team members if they are setting up these automations as well as manually inputting data to CRMs or data management platforms. Doing so ensures that the very first step of data management is done right, thus preventing further problems down the line.

Mistake #3: Treating the IT Team as the Hero Working in Silos

The common practice in many businesses is to treat data management as an IT function. In other words, data management issues are often perceived as problems that the IT team must deal with, and they have nothing to do with the business itself. While this may seem like a logical move, it is a very atomistic approach. This is because most data management or stewardship issues of a company are linked to business processes.  For instance, the majority of the root cause analysis tools of data quality are linked to the business.

Hence, to ensure that a company’s data management effort is truly effective, data should be treated as an organizational asset, and data management or data stewardship should be treated as a business role. Henceforth, it is only wise if the data managers or data stewards handling the process are familiar with the business data, including the origin of the data and the business objectives, so they can utilize the data in the best possible way to drive organizational change. Alternatively, a company should train the data stewards by equipping them with the knowledge of the business data and its utilization in the business.

Mistake #4: Sacrificing Data Security

Naturally, when working with big data, there will be security risks involved. And you would be surprised that many businesses brush the importance of data security aside, assuming goodwill with the security of their data. In today’s digital market where data can be easily stolen and hacked, there should be at least firewalls or data management software that can help ensure the safety and security of data in place. Furthermore, with the recent update in the General Data Protection Regulations (GDPR), data security has been said to be a significant deal breaker in customers’ purchasing decisions. So, there is no reason for you to neglect prioritizing data security and safety in your business, and ensure your company follows all guidelines set by the GDPR.

Below are the recommended key measures that can be easily done to keep your company data secure:

  • Access control: Specify privileges and accesses for each type of user on all your software that involve sensitive information, including customer data and billing information, to prevent the abuse and piracy of credentials.
  • Backup and recovery: Always backup your data from time to time and have a recovery plan in place to ensure the continuity of data in the long run. Backups can also be automated to save time and effort.
  • Encryption: Use encryption in all your data. Encryption is a conversion of readable data to meaningless codes, which can only be deciphered once decrypted by those with access. Encryption is the fundamental building block of data security as it ensures data cannot be simply accessed by unauthorized individuals.
  • Maintenance and contingency plan: Database should be maintained regularly, and a company should put together a plan with clear measures to take when a suspected data breach is detected. A preventive measure is better than a cure.

 

Finally, while understanding the key measures and steps to better data security is important, everything is futile without choosing the right data management and analytics tools which go beyond data collection and data integration to include comprehensive data security management systems, as well as upskilling your team. Educating your team with the basic security knowledge and issues can make a huge difference in securing the data of your company. You do not want a strong firewall against external threats but data to be leaked and misused within the four walls of your company. In short, data security should be at the forefront of every business and not a reflection to be done in hindsight.

Mistake #5: Self-Service Business Intelligence as a Double-edge Sword

Finally, with the ubiquitous rise of mobile business intelligence tools that allow data to be accessed on-the-go, and data management which was traditionally a niche field is now available to business users with zero technical know-how. This shift in how data is consumed and managed is supposed to spur democratization of data. However, if not careful, this trend can also create an environment within a company where there is no transparency and shared data for the larger organization. For instance, self-serving business intelligence can result in neither IT nor the business team taking ownership over data sharing initiatives to the larger audience.

As data management tools become increasingly accessible, businesses should also commit to building a data literate culture within each company. Data literacy is the ability to understand and communicate data anywhere, anytime, and it is a skill to leverage on business data. A company where only the top management level has access to business data and its analytics would be slow to innovation and growth. Data literacy that permeates different levels of a company allows employees, not just employers, to make more informed decisions that are aligned to the business objectives, as well as understand the importance of maintaining data quality and data security in every action they take. In sum, it is not just a mistake, but also a missed opportunity if a company uses self-service business intelligence as a gateway to bureaucracy rather than data literacy within an organization.

With these solutions to the top data management mistakes in your arsenal, you are in good shape to bring your company’s digital transformation to the next level. All the best!

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