The answer is almost certainly yes.
If you were planning a road trip, surely your first step would be to look at a roadmap and determine two things:
- Where you want to go
- How you’re going to get there
The same logic applies when it comes to making data-driven business decisions, yet many companies end up doing the equivalent of driving wherever the road takes them without planning their turns, their milestones or their necessities for the trip – they just cross their fingers and hope to end up somewhere good.
If your business is never going to create or touch any data, then you won’t need a data strategy (obviously). But if you plan on using data, producing data or otherwise keeping pace with technology, you need to have an analytics strategy and roadmap in place to store, protect and capitalise on the data you have access to. This is the case regardless of whether you’re B2B or B2C, or whether you’ve got 5 employees or 5,000.
Small and medium organisations need a data strategy too
There are a number of misconceptions around data strategies, one of which is that they’re only necessary for tech-heavy companies with huge, sensitive databases and high analytics budgets. In reality, it’s important for even small companies with few customers to ensure they’ve got their data storage, governance and analytics plan sorted - doing so early on will prevent a lot of headaches down the road.
Here are some examples of different use cases and areas of importance depending on company size and unique challenges:
- Small company: Julia, who owns a three-employee bakery, creates a data strategy and an analytics roadmap because she wants structure around using her data to decrease wastage and increase sales
- Medium company: Chris, the head of IT at a 50-person insurance company, creates a proper data strategy to fix the disparate policies and existing documents around data governance and storage
Large company: Jane, the CIO of a 500-employee travel company, and her team workshop a data strategy and analytics roadmap to help them streamline their information storage and plan for analytics optimisation
As you can see, there are many use cases highlighting the importance of analytics project planning and strategy regardless of the size and nature of your organisation - creating a data strategy, and actually using it, is a key component to ongoing success in an increasingly data-driven world.
But why do I need a data strategy?
To succeed at analytics, it’s important to have defined both your organisation’s challenges and aspirations. You need to know where your business is going and what your data and analytics capability would need to look like if you get to that place. You need to define the key milestones and workstreams that need to be put in place in order to enable you to create value from your data.
If you don’t spend time up-front outlining your data analytics plan, you’ll end up facing a heap of challenges – such as the ones outlined below.
What happens when you don’t have a data strategy
There are a number of problems that often present themselves in the absence of a data strategy. Do any of these sound familiar to your business?
- Difficulty accessing data and uncertainty about what data is available
- Data quality issues and poorly understood data standards
- Unclear responsibilities and disparate systems across teams
- Increasing dissatisfaction with IT
- Lack of understanding in the business around the importance of data
- Inability to get analytics initiatives across the line and delivering value
What are the components of a data strategy?
A data strategy has three overarching sections that will enable analytics value capture and ensure ethical compliance:1. Optimising the key workstreams to build analytics capability: this section involves filling and prioritising the pipeline of work, ensuring you have solid problem statements you can reference through every stage of the process.
2. Creating technical environments that enable progress: there are three main ‘environments’ to create if you want to truly push the need – exploratory, prototyping and production.
3. Solidifying legal and ethical guidelines: it’s important to outline the rules and principles around data ownership and usage before getting started with analytics.
How do I create a data strategy?
If you’re keen to read more about Datamine’s take on the steps involved in each of the above areas, check out the Datamine Guide to Data Strategies below. Matt Wilkins has outlined the different components of a data strategy (or analytics blueprint, as he calls it) designed to help businesses that are struggling to see value from their data. Download the free guide below.