Every day your company adds new data to the already substantial amount of existing data stored in your ERP, CRM and other IT systems is already enormous. Data doesn’t provide deep insights on its own, but when it’s used correctly it can uncover actionable knowledge that can improve processes, assess financial fitness, enhance customer satisfaction and provide other metrics that predict business success or warn of potential threats.

What is a data strategy?

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A data strategy is a goal-oriented procedural plan that takes stock of how your company currently collects, stores, analyzes and uses data. It seeks to eliminate common obstacles like siloed data and inefficient data management practices to turn data into an asset that meets your business goals. This approach aligns data management with business strategy, data governance, compliance and security.

Why is a strong data strategy important?

A data strategy will help your company use data to increase its agility and maintain a competitive edge. If it is well thought out, your organization will be able to make better decisions, minimize risk and identify new business opportunities. Data protection and security are easier to maintain and collaboration between teams and departments is simplified and optimized.

Today many companies have a data-management function and a new role, chief data officer (CDO), has emerged. Some companies have already created these positions, and it is clear that a well-structured strategy is crucial to safeguard and utilize your data effectively. Yet, numerous businesses are lagging behind their peers.

Get ready to become a data-driven company

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Even if your company has been dealing with big data for years and started many projects and tried out new tools, you may be unprepared for the increased complexity and sheer amount of data that you have to manage. This is where a data strategy provides clarity, optimal use of resources and a focus on the most efficient projects.

Before you outline your initiative, get an understanding of your current analytics maturity level. In its Analytic Ascendancy Model, Gartner defines this data paradigm into four phases:

Descriptive

What happened? This stage involves the use of business intelligence tools, data visualization and dashboards to analyze what has already taken place. It answers questions like how much did we spend with a particular vendor?

Diagnostic

Why did it happen? This stage uncovers the cause of why certain results were or were not achieved. For example, it delves into the behavior of salespeople who are on track to meet their quota versus those who aren’t.

Predictive

What will happen? This type of analysis enables forecasting possible outcomes. It analyzes what is likely to happen.

Prescriptive

How can we make it happen? At this final stage of maturity companies can generate outputs that guide them in choosing the best course of action.

Once you identify the phase your company is in, you’ll be able to set attainable goals and take realistic, incremental steps to become more data driven. Start with an inventory of the tools, technologies, and systems you use today. Then complete a comprehensive overview of your data infrastructure and existing data architecture.

As part of the strategy development process, define clear objectives, corresponding timelines, the expected use of resources, and the technical and legal framework. You also want to analyze whether your workforce has the necessary knowledge and skillset to carry out this effort or whether you need to supplement it with targeted training or input from outside consultants. The success of your projects hinges on whether you have the right people in the right roles.

How do you develop a data strategy roadmap?

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A data strategy requires a focused, down-to-earth approach. To keep everyone in the loop, all employees involved must understand the strategy and their part in creating the roadmap. The strategy development team should include all relevant departments to promote collaboration between employees with different professional backgrounds. Ideally, the project is led by someone who already has experience with the targeted data processing of data and big data and who understands the company’s strengths and weaknesses. External experts can also be called in at this time.

Let's take a look at the development steps:

Step 1: Describe your vision

Every data strategy is individual, as it’s tailored to each company's initial situation and goals.

This step is about answering the following questions:

What is the current market situation and how does your business model work?
What do employees and management imagine in the company’s future?
What specific goals are to be achieved with the new data strategy?

Step 2: Identify data sources

Companies need to collect, cleanse and prepare data that is already generated on a daily basis. This step includes documenting all data sources and ensuring that the workforce is aware of the data sources and their documentation. This is where IT administrators, data engineers and other employees assigned to develop the data strategy can provide support.

Questions that should be considered in the course of this analysis include:

Which internal and external data sources are available and accessible today? Which are merely available but not yet tapped?
Is the stored data accessible to all departments? This is particularly important to avoid the creation of data silos.
What is the status of data protection and security?

Step 3: Defining integration points

Companies work with two distinct types of data. Structured data is organized, indexed and easily searchable. It is quantitative and can be contained in fixed fields and columns in databases and spreadsheets. Examples of structured data include corporate financial records archived in software like Sage or QuickBooks and employee and customer information. On the other hand, unstructured data is qualitative and doesn't follow a uniform format. Unstructured data includes incoming invoices, resumes, tax documents and emails. Using data effectively means incorporating it into information flows. In many companies, however, just 1% of unstructured data is actually used. To do this, the individual data and its sources must be linked together. And it is precisely this process that the third step of strategy development deals with.

Staff on the data strategy team, data engineers and data scientists deal with questions such as:

Which information workflows already exist, which data sources will create new ones, and how can they be integrated?
How can data be linked in such a way that new information is created from it?
Which tools are suitable for this?
How can the new information be used to increase profitably?

Step 4: Tools for data-supported decision-making

When companies can turn information into business-relevant knowledge it becomes an important business asset. This step sheds light on which information can be transformed into business intelligence. This requires the input of subject matter experts and a data scientist.

At this phase, it’s time to address questions like:

How does data help to make key activities, the use of resources and costs more efficient?
How can marketing, sales and delivery channels be optimized?
What value propositions can be based on this data?

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Step 5: Planning the implementation

Now that it is clear what data is available, how information is created, and how business knowledge can be extracted, develop a plan to implement the data strategy.

Clarify the following points:

What integration solutions are needed to connect internal and external data sources?
Should this software be purchased or developed in-house?
Which analysis tools will be used and how will the results be formatted?
What skills do employees need? Are training or workshops necessary?

Step 6: Data strategy

Now that all questions from vision to implementation planning have been answered, the final step is to formulate the ideas, concepts and expected results. The data strategy lays out this plan transparently and is also the basis for convincing employees, partners and managers alike of the project’s value. Responsibilities can be assigned employees and their teams.

You should clarify:

Who will design analysis processes and who will ultimately conduct the analyses?

What format will be used to deliver the results?

What do the data analysis workflows look like?

Who handles each part of the project?

How do you monitor performance?

Your company can apply these measures to assess your progress:

Establish Key Performance Indicators (KPIs): Define clear key performance indicators (KPIs) to help track the progress of the data strategy and ensure that it is optimally aligned with business goals.
Ensure data quality and security: Schedule frequent reviews to ensure that the data strategy is being implemented effectively and that data integrity is maintained.
Review and adjust regularly: Review and adjust the data strategy as necessary to make sure it remains focused on meeting current business objectives.
Create governance and compliance structures: Ensure that the methods used in the data strategy complies with legal requirements and minimizes the risks associated with processing data.
Continue to engage stakeholders: It is important to involve stakeholders, including customers, partners and employees, and use their feedback to fine-tune your processes.

How does document management come into the picture?

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A document management system (DMS) can play an important role in your data strategy because of its ability to manage structured information and turn unstructured data into a usable format. A DMS manages the entire lifecycle of documents and data within an organization and replaces repetitive, manual tasks with automated workflows.

These systems handle the capture, reading and indexing of information; provide opportunities to annotate, edit or improve the information; and offer robust workflow and automation tools to ensure that information gets to the right people at the right time. In addition, comprehensive audit logs and analytics capabilities record who accessed, changed or removed documents and when.

A DMS and its automated workflow functionality enables quick, straightforward confirmation that employees are following company policies while reducing the time they spend on repetitive routine tasks. Adopting digital document management offers reporting capabilities so you can catch issues before they become problems.

Regulatory compliance is easier and data integrity is maintained

Rights-based access restricts which documents and index data users can store, retrieve, edit, modify and remove from a digital file cabinet. Sensitive data is protected to ensure its authenticity as well as to block unauthorized overrides and workarounds. An audit trail helps prove compliance and makes it easier to enforce company policies and ensure that state, federal and industry-specific regulations are followed.

Data privacy is of increasing concern to every organization. In addition to current compliance requirements outlined by HIPAA, GDPR, the Shield Act, COPPA and other regulations, be prepared for new and potentially tighter ones to come along in the near future.

With a document management system, you can ensure high data integrity and prove that information is complete, accurate, and stored with administrative, physical, and technical safeguards to ensure it is not inappropriately altered, damaged or deleted. In addition to providing internal security, the solution can safeguard customer and membership data. Electronic processes also adapt quickly to changing regulations or new laws. And centrally enforcing company-wide policies is much more efficient than implementing them department by department.

Advanced security and disaster recovery planning are key pillars of a compliance strategy. With a DMS you can set up clear security and privacy requirements and consistently meet them. These platforms supply state-of-the-art document and internet communication encryption that help protect cloud services against protocol downgrade attacks, cookie hijacking, malware and other cyberthreats.

DocuWare experts can show you the best way to integrate document management into your data management strategy.

Learn more about document management.

How to Create a Data Strategy Roadmap

What is a data strategy?

Why is a strong data strategy important?