We are in the process of developing Data management strategies. What is the suggested scope we can start with and what is the recommended roadmap?
The roadmap's first phase should consist of a thorough data audit. You'll need to must first identify all the data sources in your company, learn what kinds of data each one stores, and evaluate the quality of the data they provide. The state of your data landscape will become very evident from such a simple audit.
Next, write out some rules for how to handle data. Data management entails developing protocols for the entire data lifecycle, from collection to storage to access to disposal - this also includes identifying roles via a RACI. Data management solutions may be used when a reliable governance structure has been established. A data warehouse or data lake may be used for centralized data storage, and ETL tools can be used to integrate data, and data quality tools can be used to clean and enrich data.
The next stage in keeping your data safe is putting in place safeguards like access limits, encryption, and routine audits - there's lots of software available for you that can do this.
Lastly, data management strategy does include improving your data management in an iterative process. This requires keeping up with the newest developments in data management technology and trends, and having frequent reviews of your data quality and measuring the efficacy of your data governance rules.
Keep in mind that developing a data management strategy is not a one-time event, but a journey that adapts to the changing demands of not only your business, but technology advancements.
- Inventory your data assets
- Give a priority or criticity to each data assets (will help to sequence your actions)
- Determine if a data asset is subject to: compliance, regulatory, privacy
- Classify your data in respect of its sensibility: public information, sensitive, confidential, secret
- Systems that own or manage the data
Then, at least you know in what kind of trouble you'll get in ;-)
And I agree with Donna, don't fall into tooling first.
As for strategies to develop:
- Which data assets are of broader interests for the organization as opposed to the ones which are more 'operational'
- Develop or enhance your data retention policy (never too late to double-check this)
- Elect a CDO (Chief Data Officer)
- Think of a 3 tiers data architecture
1- Operationel data used by the systems or processors (this is in silo, systems per systems)
2- Data that could be transfered to a common place (a datalake)
3-Data that could have a strategic value (might need some effort to normalize)
Each time, less and less data make it to the next level
- Wonder about master data management, customer unicity across multiple systems,etc.
Should keep you busy for a few months ;-)
Steve
Content you might like
Accountability - There's no system for accountability - we just rely on people keeping their word33%
Innovation - There's a structured process to contribute an idea and see the eventual outcome and decisions53%
People - Our company finds it difficult to do any of the above33%
People - Laggards hold things back but certain people and teams make it happen31%
General - We find it difficult to do any of the above15%
IT - We are held back from most of the above by legacy systems and a dependence on IT24%
Processes and Workflow - We've reached a point where email, chat and documentation have been replaced with accountable tasking and repeatable processes17%
Processes and Workflow - We publish processes or documentation and try to keep it up-to-date13%
Something else (comments below)1%
Yes43%
No57%
If that is correct, then it can be extremely hard to price.
Most ...read more
avoid tool first strategy. take an inventory.