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?

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Senior VP & CISO, 1,001 - 5,000 employees
Before scope, I'd ask what the business objectives are? Then create scope and team that considers alignment of the strategy with corporate vision and goals, governance structure and roles and responsibilities, and metrics / measures of success.

avoid tool first strategy. take an inventory. 

Director of IT in Travel and Hospitality, 10,001+ employees
On a high level, you will have to identify your needs first, is it integration, master data, data models analytics, etc. After you list the organizational needs, you have to define what is the priority of each of these needs and what would be your desired sequence of implementation. How many applications do you have in the ecosystem of application footprint? How is the integration being handled currently? Do you have any challenges?  What are the challenges? Are the integrations redundant or unique, is it required online or batch process ?  Once You have answers to the above questions then you will automatically be in a position to define the Data Management Strategy.
Director of IT in Healthcare and Biotech, 10,001+ employees
Identifying your organization's objectives and the function that data plays in getting you there should be step one in developing a data management strategy. You'll need to identify key stakeholders and what their expectations for data are, along with how you'll measure success. 

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.
Strategic Banking IT advisor in Banking, 10,001+ employees
I would start by a few simple actions:
- 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 ;-)


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