5 Data Management Mistakes Small Businesses Need To Avoid
As big data applications are expanding daily, more and more businesses are shifting their focus to digital transformation. It has been seen that the organizations are promoting the data and are treating it as business assets.
Despite big data having the potential to leverage the outcome for the business, it has failed to draw any fruitful attention in the last couple of years. This can be due to the lack of trained professionals who are equipped with the skills to transform big data into meaningful insight.
In recent years, many enterprises have tried to lead the initiative to revamp big data technology and help it reach out full potential, but looking at the initiative’s success rate is quite disheartening.
Why is that so?
Well, they all have been making some common mistakes that have acted as barriers to reach their goals. With that being said, we have prepared some data management mistakes that need to be avoided at all costs.
Top Common Data Management Mistakes
Investing in data is lucrative, but investing in unorganized data can result in a huge mess. According to Gartner master data management, such situations can be avoided easily if you are aware of these common mistakes.
Absence Of Data Governance
Before proceeding with the whole data management thing, it is necessary to ensure that you have an effective data governance framework ready to keep the complete life-cycle in check.
This can be done only if the organization has a governing body controlling the big data flow and oversees the whole process with proper data administration. Once this step is initiated, under no circumstances should this step be missed.
Not Paying Attention To Data Architecture
For most people, data structure might be a new term. Well, the following points might clear the concept.
- Making no investment in architecture and tools.
- Limited investment that lacks dedicated practice.
- Lack of collaboration between architectural and other essential processes.
- Having no architectural methodology at all.
Ignoring The Data Quality
Data governance can only be considered a success if the quality of data is maintained consistently. The quality of data plays an important role while making business decisions. In fact, data integrity can be ensured if the quality of the data is secured.
Business decisions are data-driven; hence, if the quality of data is poor, it affects the business decision that directly affects ROI. When data quality is maintained, the standardized data evaluation becomes a reality.
Continue To Practice Poor Data Profiling
If you truly want to create a world-class data integration application, data profiling is something that you can fall short of. It is a common practice that ETL developers follow.
As data is predictable and can change at any given time, flexibility in the strategy and dataset is essential. In-depth data profiling at the start of a project ensures fewer updates while working on the projects.
Collected Unnecessary Data
Data governance is all about having a good data retirement strategy. Not sure what this means? At some point in time, every piece of data in the organization needs to be recycled; if not done correctly, you might face data duplication and disorganized data.
Hence, the organization has to invest time and energy to ensure every piece of data is organized and unique. According to Veritas Global Databerg, more than 85% of the stored data is either dark, obsolete, or trivial.
When going with the data management process, it is very important to take small steps. This ensures that every data you collect remains relevant to the new trends. So, instead of making a database and data management system to handle big data, why not manage small data more efficiently.
The best way to go around data management is to start with resolving current problems. Take small steps at a time. This practice will ensure that you do not falter and collect useful data every time.