Look, when it comes to data, it’s best to act strategic and proactive instead of ad hoc and reactive. Data management includes all the processes for proactively collecting, organizing, protecting, storing, and sharing data. If your company understands the importance of data for decision-making, then chances are high that you already have some tools and processes in place for managing your data. But how well do these tools and processes work together? Are there gaps or places of overlap? Do these data management activities align with your enterprise’s overall goals? And is your data being used as effectively and efficiently as possible? If you answered these questions with doubt or uncertainty, you or your organization might benefit from an enterprise data management strategy.
What is a data management strategy?
Put simply, a data management strategy is an organization’s roadmap for using data to achieve its goals. This roadmap ensures that all the activities surrounding data management—from collection to collaboration—work together effectively and efficiently to be as useful as possible and easy to govern. With a data management strategy in place, your company can avoid some of these common data challenges:
- Incompatible, duplicate, or missing data from undocumented or inconsistently documented sources
- Siloed projects that use the same data, yet duplicate the efforts and costs associated with that data
- Data activities that consume time and resources but do not contribute to overall business objectives
A data management strategy will be the strong foundation needed for consistent project approaches, successful integration, and business growth.
5 steps to an effective data management strategy
If your company faces these kinds of challenges, it’s time to develop an enterprise data management strategy. Fine-tuning and finalizing a strategy that works best for your business will take time, but you can start with these five steps.
1. Identify business objectives
Your organization creates billions of data points per day. If you don’t let your business objectives inform your data management strategy, you could waste valuable time and resources collecting, storing, and analyzing the wrong types of data. It is usually helpful to ask questions like:
- What are your organization’s overall objectives?
- What data is needed to meet these objectives?
- What types of insights and information are required to make progress against these initiatives?
Focus on the three to five most critical use cases for your company’s data and build your strategy from there. With your business objective at top of mind, these priorities will help determine processes, tools, governance, and more.
2. Create strong data processes
Now that you know how you will use your data, it’s time to think through the processes in place for collecting, preparing, storing, and distributing the data. Begin by identifying the owners and stakeholders for each of the following data management activities. The questions below are a great place to start as you consider each step of the process.
Collect
- What will be your data sources?
- Will you need access to both external and internal assets?
- Do you need structured data, unstructured data, or a combination of both?
- How will the data be collected?
- Is this a task that will be done manually as needed or will you set up extract scheduling?
Prepare
- How will you clean and transform raw data to prepare it for analysis?
- How will you identify incomplete or disparate data?
- What will be the guidelines for naming data, documenting lineage, and adding metadata to increase discoverability?
Store
- Where will you store your data?
- Will you use XML, CSV, or relational databases for structured data?
- Do you need a data lake for unstructured data?
- How will you keep your data secure?
Analyze and Distribute
- Which teams or departments need the ability to collaborate?
- How can you make access to data and analysis easier for the end-user?
- How will you communicate any data insights?
Create beautiful visualizations with your data.
3. Find the right technology
As you work through the questions above, you might find that identifying the right tools or platforms or technology solutions is essential to building a data management strategy. Think carefully about what hardware or software you need to establish a strong data infrastructure. The Tableau Data Management Add-on helps companies manage the data within their existing analytics environment, streamlining the process so people get the information they need when and where they need it—directly in the flow of their analysis. From data preparation to cataloging, search, and governance, the Data Management Add-on helps ensure that trusted and up-to-date data is always used to drive business decisions.
4. Establish data governance
The increased use of data and the growth of your data infrastructure brings not only big benefits but also a big responsibility. Don’t skimp when it comes to establishing data governance, and take the time to create and communicate policies and procedures for proper data usage. Some themes to explore:
- Data quality: How are you ensuring that data is accurate, complete, and current?
- Data security: What steps are you taking to securely store data?
- Data privacy: Do you have permission to collect and use data?
- Data transparency: How do you foster an ethical data environment?
Data governance ensures that data is used correctly and consistently across the organization, so policies and procedures should not only be communicated and understood by owners and stakeholders, but by everyone in the company. This is a great step in fostering an organization-wide data culture.
5. Train and execute
Sometimes the biggest challenge in using data effectively is that the organization’s data owners are not data experts. A critical part of your data management strategy will be to provide the knowledge and skills your team needs to analyze and understand the data. This could mean putting data analysis tools in the hands of departments outside of IT or getting buy-in from your organization’s leadership so they’re ready to support your data initiatives. Whatever this looks like, make sure everyone understands the company’s data management strategy and how to successfully execute their role.
Data management strategy example: REI
REI used Tableau to implement a data management strategy, which helped to personalize the customer experience and gave them a competitive advantage. While they didn’t mention data management by name, take a look at how they executed each step in the above process:
- Identify business objectives: REI’s primary business objective was to provide a more personalized customer experience. They identified several ways data could help them accomplish this objective, such as analyzing customer segments to inform decisions like shipping methods and product category assortment.
- Create strong data processes: REI identified 75 sources of data and uses 1,200 dashboards, so fostering a collaborative culture between IT and business units was important to success. To nurture collaboration, the teams committed to measures like holding monthly meetings face to face, distributing weekly project updates, and scheduling a collaborative requirement gathering.
- Find the right technology: REI moved from housing billions of rows of data in Excel to doing advanced analytics in Tableau. Teams across the organization now use Tableau dashboards to uncover important insights and apply them toward an improved customer experience.
- Establish data governance: REI business analysts curate the data and make it available to subject matter experts, with IT governance and support.
- Train and execute: REI evangelizes and educates its audiences (i.e. the analysts, managers and subject matter experts) on data insights in a few ways. Examples include: lunch and learns, “data literacy” sessions, one-to-many “reporting the news” sessions, and more.
Make Tableau a part of your data management strategy
If your organization wants to be more purposeful with data and drive efficiencies, laying a strong foundation starts with a data management strategy. Tableau can help break down data silos, streamline processes, and make self-service analytics accessible across your organization. The Tableau Data Management Add-on works seamlessly within the Tableau’s analytics platform to integrate management processes and increase the visibility, reliability, security, and scalability of your data.