Nine best practices for Data Management to start with
Every company sooner or later thinks about how to get best use from their data. And what to start from? World know enterprises use standards like ISO 8000, best practices like Master Data Management(MDM) and MDM automated solutions or adopt framework such as ITIL or Six Sigma, which as well cover efficient data management.
But what if your company and your team is at the very beginning of this way and the only guidance is your existing problems which push you for improvements. All the methodologies and standards are very good and cover the consistent data management fully, but you need something to get maximum utility with a minimum of investment for quick start.
Based on the experience during work with telco company we will share 9 “must take” steps which are critical for efficient Data Management to start.
1) Create Data Management strategy document.
It is the starting point from which your company is going to change for better and more efficient data management. The aim of the strategy is to set data management principles for the related processes, tools and solutions that consistently define and manage the critical data. The document is crucial, as it should be a baseline for data management of your existing and new solutions. As well it will provide valuable input for your existing and new vendors which should follow it. The document should cover but not limited such areas like: business approach toward data business use and data itself, data quality principles, prefered database management systems, prefered storage solutions and data types etc. Ideally, Data Management strategy document should be part of IT Service Management(ITSM) strategy. And if you have no ITSM strategy, it is the best time to initiate.
2) Define what is needed and for whom.
Who are data producers and who are data users? Get business and users involved. What data is important for your business? What is the strategy plan for data use? There is no need to keep all the data, as cost of storage matters. Involve both business and users to cover those needs in storage what really matters.
3) Inventory and data knowledge tracking.
Build a data inventory with clear details on where the data exists, how the data is used, and who owns each data set. It will help to determine standards across the company and provide clear data origin rules when it comes to data quality cleanup work. As well keep all the knowledge related to inventory and data management in one place in consistent way. WiKi based solutions best suites this.
4) Keep data in order. Always!
Put efforts to avoid temporary solutions in data management. There is nothing more temporary than a permanent solution:). But for efficient data management it is terrible issues to solve.
5) Keep the data secure.
Secure access to data. There should be defined approaches to manage the confidentiality, availability and integrity of the company’s data. It should be established via who, how and when should have access to data.
6) Backup the data.
The rules and solutions for backup should take place before the data is produced. Use it as a dogma. If a data is not critical for backup, then no need to store at all. Again, cost of storage matters!
7) Step by step approach.
Think big, but make step-by-step implementation. Invest sufficient time in planning, evaluation and implementation. Estimate intermediate results and continuously inform stakeholder.
8) Best practices and standards.
For the beginning there is no need to asses all the best practices and standards for efficient company’s data management. But in long term it will be the good support, and you need to think about it in the beginning. It could be initiative team or even a person who will be acknowledged with these practices. It could be a long term training plan or some employees individual development plan. But there should be an internal competence raised in long term.
9) Continuous improvement and long term strategy for data usage.
Do not stop on achieved results. Overview them and determine what should be next. What else can we do to improve data management and increase business value out of it? Your data is valuable strategic asset which should be used accordingly. And this value hardly could be fully achieved in one shot. Therefore continuously seek for extra needs for data management to add extra value for business.
All these steps will provide a solid ground for Data Warehouse, data analytics and data mining to get valuable business insight. As well as provide you with competitive advantage on the market.