As enterprises increasingly embrace data, remaining relevant and competitive will be determined by how skillful the organization is in identifying and using data, implementing new technologies, and applying effective data analytics strategies. This can only be made possible with a solid data strategy that supports business decisions and transforms business operations.
The global data market revenues for data and business analytics are expected to reach approximately $274 billion in revenue in 2022. Another analytics survey by McKinsey shows that nearly 50% of organizations indicated that Big Data and analytics have primarily changed business operations in their sales and marketing functions.
With such high figures, it’s clear that data has revolutionized the way enterprises run their operations. This represents a huge opportunity, an opportunity that also comes with challenges.
To help you refine your approach to data, here are a few reminders about why you need a solid data strategy along with data strategy questions you need to start asking to ensure success.
Why Have a Data Strategy?
Having a clear data strategy is critical when you take into account the sheer volume of data available today. Many organizations get caught up in the big data buzz. They collect too much data, without a solid plan of what they intend to do with all of this data.
Others are overwhelmed by options. They rush to invest in emerging technologies and expensive data analytics tools that turn out to be a bad investment. In most cases, this trend is accompanied by prolonged delays or a compulsion to overlook the data revolution. In these cases, most organizations fail to realize the full value and potential of data.
For this reason, enterprises need a data strategy framework to:
- Identify the business problem.
- Prioritize the backlog.
- Create a strategy to solve the issue.
A data strategy should include the type of data the team needs, where and how to get that data, and how the team plans to store and analyze the data. With a data strategy framework, it becomes easier to narrow down the focus to specific organizational needs and formulate an attainable plan.
Data Strategy Questions You Should Ask
A strategy is only as good as the questions you ask as you are building the strategy. Self-analysis is not something busy teams do enough, so it’s up to you as the CDO, CIO/CTO, or CFO to take on this lead analyst role…starting with these key data strategy questions.
1. What insights do we want to achieve?
Self-analysis begins with the end result when you are building your data strategy. What do you hope to achieve with your data? Once you know the end result, you can work backward to figure out the starting point.
When working with data, ask yourself the following questions:
- What data do we already have?
- What data do we need to obtain?
- Why do you need certain data?
- How can you manage data?
2. Do we have a data strategy vision statement?
You already have a company vision statement and you also need a more specific one to align your teams around a common purpose…a data strategy vision statement.
Data is a mindset and this data-driven mindset should be cultivated throughout the organization. By creating a data strategy vision statement, you are giving direction and providing a sense of purpose.
Unlike a mission statement that focuses on what you’re doing today, a vision statement focuses on what you need to accomplish in the future. What are you inspiring to change? Having a data strategy vision statement clarifies the vision for your team, helping people prioritize tasks along with short-term and long-term goals.
3. Are we meeting unique departmental needs with data modeling?
Now it’s time to evaluate the well-being of your organization so you have a clear picture of whether your data strategy and data governance aligns with the unique needs of every department.
Data modeling techniques play a crucial role in both the sustainability and growth of any enterprise, especially when it comes to making data-driven decisions. The data architect uses data modeling concepts to create a model that features a data warehouse that meets the unique needs of various departments.
The result of the data modeling process is a formal presentation of the database structure that includes data items, the relationship between these data items, and the constraints on this data. Having your data in the right format makes it easy to understand so data is used as a reference point that ensures all user requirements are being met.
4. Who’s navigating the ship?
It’s easy to get lost in a sea of endless reports, which is why you need to focus on what makes a data strategy truly successful—people. And, which people are navigating the ship.
The answer to who should take charge of the data strategy is different for every organization. Leading a data strategy involves bringing together stakeholders from different areas to think critically about the organization’s goals.
Your data strategists should have cross-functional knowledge and the authority to drive change. The ability to develop relationships with key stakeholders will be crucial to the success of your strategy. Keep in mind that the data strategy framework is a business-driven process implemented with the help of a tech team. Consider choosing someone in a business role, rather than an IT role, to keep the process focused on meeting business objectives.
A successful data strategy also requires collaboration across the organization. Make sure you have a strong supporting team to execute the data strategy and help with data governance.
The road to achieving a successful data strategy requires time, effort, collaboration, and commitment from C-level leadership. Knowing the right data strategy questions to ask will help you define an action plan that yields results.
At Collectiv, we empower the C-Suite to define and implement successful data strategies. Contact us if you need guidance.