Common Data Management Mistake You Should Avoid

The new oil of the digital world is data. The one who controls it is the emperor of his industry. The one who manages it excellently is the leader at the top of the list. Data brings you the insights you need into the industry and can make excellent decisions. It can help you reduce risks and to build insightful strategies to carry on operating a business. the leaders of the respective industries have already incorporated the best measures to manage and control data. The other players are following their shown path.

These companies need a consolidated platform to gather data, analyze it, and manage to bring out the right information for making the future path risk-free and resourceful. Despite availing of the best measures, the companies often tend to make the gravest mistakes that cost them dearly. If not recognized, it can be a devastating factor for the digital infrastructure of an organization. In this section, we will discuss what common data management mistakes you should avoid.

Common Data Management Mistakes

  1. Improper Data Governance

A mentioned earlier, the volume of data generated is huge. In fact, it will increase multiple times within a short time span. This is where the challenge is. Companies will have to be very intuitive in developing a proper platform for brilliant data governance. Those who can manage and govern it well win the race in the future.

Proper data administration can be incorporated into data management when the right professionals are recruited. Most companies outsource the requirement to an experienced data consultancy service for proper administration and management. A complete check of the data lifecycle will be done to ensure proper implementation and administration of the measures.

2.Ignoring Data Architecture

Data architecture might sound confusing to inexperienced or unskilled professionals, but it has immense importance in data management. The gravest mistakes a company can do, in this aspect, is not investing in setting up a proper data architectural platform incorporated with the latest tools. In fact, not making periodic reviews at a regular interval can show a lack of dedication in practice. Improper integration between the essential processes such as portfolio management and the data architecture can also cause havoc.

This is where a limited investment with the optimum utilization of proper resources can also be fruitful. It is not that heavy investment in data architecture is required. Just hiring the right data management service will do the trick.

3.Ignoring the Dynamism of Data

Data is ever-changing and evolving continuously. It means a particular set of data with a particular definition will become irrelevant when a new trend hits the market. This is where a data management platform will have to understand that data governance is not a conventional project. It is a part and parcel of business operations.

The best solution, in this domain, is defining a set of data project streams concentrating on a key area. Hence, it is not a project that will end at one point. It is a dynamic and continuous process that has to be a part of the business operations throughout the lifespan of a company.

4.Quality of Data

Data can be impure too. Unnecessary data from unwanted sets can make the process dilute. Hence, the results from the analytical platforms will be impure and not to the point. It is tough to concentrate on such results to make decisions. This is where data quality has become a prime issue that most companies tend to ignore.

Business decisions are made based on the data drawn and analyzed by a company. If the data quality is not appropriate, it can generate wrong information. Correct data collection and maintaining its purity are mandatory. This is why businesses hire experienced data management services to ensure the purity of data.

5.Silo Approach

A unit can be benefited from the data generated and analyzed. The definition of that data set will depend on the business unit using it. It means that the definition will change from unit to unit. This is where varied groups have different ideas of a particular set of data. This is where poor decisions are made when such approaches are considered.

In this case, data governance will be the highest priority. Data should be treated as a business asset and should be governed accordingly. The proper definition of the data sets should be designated so that each unit can make good use of them for making informed decisions.  

6.Weak Data Profiling

Data integration requires proper data profiling. For an integration application, every datum is the same and worthless when not profiled. The major mistake one can make here is improper profiling of the data gathered and analyzed. It can drop the data quality significantly.

The system should be capable enough to signify present and future data sets accordingly. This helps the machine learning platforms to analyze data and learn better. It is our responsibility to introduce the right data profile to the applications.

7.Collecting Data Unnecessarily

As mentioned earlier, the data collected by a company will increase multiple times in the near future. It will become a daunting task to manage such volume. It has also been witnessed that companies often mistakenly collect data unnecessarily leading to the impurity of the sets and a significant drop in quality. Despite proper governance, the results will not be appropriate.

Verdict

The best way to avoid such mistakes and problems is by following the steps mentioned below.

  • Define the data source
  • Check for biases in the collected data
  • Designate an experienced professional designated to check every other professional’s work
  • Reanalyzing the results on a regular basis for the verification of data integrity
  • Examining social, ethical, moral, and economic implications before starting to examine a data set

For such concerns and application of these steps, every company outsources data management requirements to an experienced service provider. This helps the company to avoid such mistakes, avail of the latest expertise and tools, and to make well-informed decisions in the future.

Author Bio: Jafar Sadhik

A passionate digital marketer possessing sound knowledge in the fields like SaaS tools, churn statistics, data management, digital photography, etc. He loves to read books during leisure and a great admirer of Agatha Christie’s works.