How to Create a Data Management Strategy In 5 Steps
Introduction to Data Management Strategy
Businesses are beginning to recognize the power of analytics, and as a result, are shifting their focus to data management. Unfortunately, many are wasting time and resources trying to gather every last data point.
However, not every dataset is relevant to meeting specific objectives and performance goals. By developing a data management strategy, organizations can optimize their data collection, preparation, and distribution practices.
What is a Data Management Strategy?
Firstly, data management refers to the various practices necessary for safely handling large volumes of information. With that said, a data management strategy is a roadmap that outlines information for businesses can make informed decisions. An adequate strategy considers all data activity, from gathering to integrating data. This way, employees can maintain a smooth workflow and improve operational efficiency.
Without a proper data management strategy, companies can face various risks that can compromise their productivity and workflow, such as.
- Duplicate, incomplete, and incompatible data
- Data systems with different formats and infrastructures
- Data silos that disjoint communication and increase operational costs
- Time-consuming data activities
- Poor resource allocation
By taking the time to prepare a comprehensive strategy, companies can establish a robust foundation for future projects.
5 Steps for Creating a Data Management Strategy
Companies that need to build a data management strategy must carefully work through the following five stages.
1. Define Objectives
Large corporations create and gather millions of data points each day, generating large volumes of information. However, not every dataset is relevant or vital to business success and can waste employees' time. Therefore, investing the time and money to gather as much data as possible can hinder productivity and profitability.
That is why businesses need to first define their objectives, including short- and long-term goals, by asking themselves critical questions.
- What are the organization's specific performance goals?
- What data is necessary to meet these goals?
- What insights can the data generate to develop impactful initiatives?
After answering these questions, organizations should have a pretty good idea of what data they need to prioritize. However, it is important to remember that each goal may require different types of information. While some businesses may require more customer data, another may need to collect internal key performance indicators (KPIs).
2. Create Data Processes
Next, the project team needs to determine how they will collect, store, and analyze the desired data. Before recruiting processes, managers need to find stakeholders that either own or facilitate the information. Then, supervisors must designate a team member for the data collection, preparation, storage, and distribution. To do this, managers should ask a series of questions for each phase.
- What are the data sources?
- Are the sources internal or external?
- Is the data unstructured, structured, or both?
- How will the team collect the data?
- Is this data extraction manual or automatic?
- How will the team clean and translate raw data into a standardized format?
- How will the process identify and eliminate data discrepancies?
- What are the data naming, documenting, and metadata guidelines?
- What is the storage method and location?
- Are the databases XML, CSV, or relational for structured data?
- Is there a separate storage center for unstructured data?
- How will the company protect data?
- What departments need to collaborate?
- How can data be more accessible to employees?
- How will employees analyze data and generate insights?
3. Determine the Right Technology
The majority of modern businesses already use digital databases, meaning they need software to gather and analyze data. However, there are numerous data management solutions on the market to filter through. In order to choose the best system for their business, owners must consider the hardware and software requirements. Depending on the solution, it may not connect with existing systems or the established data infrastructure.
Advanced data management providers offer various tools and add-ons that businesses can seamlessly integrate into their network. System integrators can even share tools alongside data, creating a universal interface. This makes data more accessible to employees and even external partners.
4. Establish Data Governance
Although data management strategies offer companies several benefits, it comes with a high level of responsibility. Owners must ensure that their data management systems are compliant with industry regulations. Otherwise, the business is susceptible to cybersecurity threats, fines, and even legal repercussions.
Therefore, organizations should invest in a reliable data governance solution that ensures they follow every data policy and procedure. This enables departments to standardize proper data management techniques, preventing non-compliance. When creating a company policy, managers need to consider the four main components.
- Data quality refers to information's integrity, completeness, consistency, and relevancy.
- Data security involves how a company chooses to protect sensitive information.
- Data privacy is the permission given to collect and analyze information.
- Data transparency is how a business chooses to foster its data environment.
By standardizing data governance throughout the company, owners can ensure consistent yields in every department.
5. Train and Execute
Even if a data management strategy is excellent, it can fail if employees don't receive proper training. After all, a plan is only as effective as the people that execute it. Therefore, project managers should analyze each team member's skills and experience to determine who should orchestrate training.
Businesses that bought external enterprise applications may require external training from the provider. Others may need their IT department to explain the ins and outs of the data system. Either way, training should clear up any confusion or concerns that employees have.