Data Academians share 5 tips to improve data management
While the challenges may seem like a nightmare, beginning to tackle them is easier than you think. Let our Data Academians, Anttoni and Pauliina, share their experiences and learnings. Though they’ve only worked at Solita for a short time, they’ve already got a hang of data management.
What does data management mean?
Anttoni: Good data management means taking care of your organization’s know-how and distributing it to employees. Imagine your data and AI being almost as person, who can answer questions like “how is our sales doing?” and “what are the current market trends?”. You probably would like to have the answer in a language you understand and with terms that everyone is familiar with. Most importantly, you want the answer to be trustworthy. With proper data management, your data could be this person.
Pauliina: For me data management compares to taking care of your closet, with socks, shirts and jeans being your data. You have a designated spot for each clothing type in your closet and you know how to wash and care for them. Imagine you’re searching for that one nice shirt you wore last summer when it could be hidden under your jeans. Or better yet, lost in your spouse or children’s closet! And when you finally find the shirt, someone washed it so that it shrank two sizes – it’s ruined. The data you need is that shirt and with data management you make sure it’s located where it should be, and it’s been taken care of so that it’s useful.
How do challenges manifest?
Anttoni: Bad data management costs money and wastes valuable resources in businesses. As an example of a data quality related issue from my experience: if employees are maybe not allowed, but technically able, to enter poor data into a system, like CRM or WMS, they will most likely do that at some point. This leads to poor data quality, which causes operational and sometimes technical issues. The result is hours and hours of cleaning and interpretation work that the business could have avoided with a few technical fixes.
Pauliina: The most profound problem I’ve seen bad data management cause is the hindering of a data-driven culture. This happened in real life when presenters collected material for a company’s management meeting from different sources and calculated key KPI’s differently. Suddenly, the management team had three contradicting numbers for e.g. marketing and sales performance. Each one of them came from a different system and had different filtering and calculation applied. In conclusion, decision making was delayed because no-one trusted each other’s numbers. Additionally, I had to check and validate them all. This wouldn’t happen if the company properly manages data.
Bringing the data silverware from silos to one place and modelling and storing it appropriately will clean the dinner table. This contributes towards meeting the strategic challenges around data – though might not solve them fully. The following actions will move you towards a better data management and thus your goals.
How to improve your data management?
Pauliina & Anttoni:
- We could fill all five bullets with communication. Improving your company’s data management is a change in organization culture. The whole organization will need to commit to the change. Therefore, take enough time to explain why data management is important.
- Start with analyzing the current state of your data. Pick one or two areas that contribute to one or two of your company or department KPIs. After that, find out what data you have in your chosen area: what are the sources, what data is stored there, who creates, edits, and uses the data, how is it used in reporting, where, and by whom.
- Stop entering bad data. Uncontrolled data input is one of the biggest causes of poor data quality. Although you can instruct users on how they should enter data to the system, it would be smart to make it impossible to enter bad data. Also pay attention to who creates and edits the data – not everyone needs the rights to create and edit.
- Establish a single source of truth, SSOT. This is often a data platform solution, and your official reporting is built on top of it. In addition, have an owner for your solution even when it requires a new hire.
- Often you can name a department responsible for each of your source system’s data. Better yet, you can name a person from each department to own the data and be a link between the technical data people and department employees.
About the writers:
My name is Anttoni, and I am a Data Engineer/4th year Information and Knowledge Management student from Tampere, Finland. After Data Academy, I’ll be joining the MDM-team. I got interested in data when I saw how much trouble bad data management causes in businesses. Consequently, I gained a desire to fix those problems.
I’m Pauliina, MSc in Industrial Engineering and Management. I work at Solita as a Data Engineer. While I don’t have education in data, I’ve worked in data projects for a few years in SMB sector. Most of my work circled around building official reporting for the business.
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