Four pillars of converting data to value
It is critical for management to have a reliable data strategy that provides a steady flow of accurate data that can be converted into real value. This data, monitored regularly, helps leaders, and businesses, make data-based decisions to drive the business forward.
As we work across multiple businesses and sectors, we have identified two critical methods for acquiring and analysing data. Put simply, these are top-down, or bottom-up.
Top-down versus bottom-up – what they mean
Top-down means evaluating what questions need to be answered, then creating a data strategy to answer those questions. A top-down approach enables you to prove the value quickly to the business. Since you are answering business-critical questions, the impact of acting on the findings is greater.
Bottom-up is looking at the data collected from across your data sources, aggregating them together perhaps with additional internal sales reports, third-party data companies, and other sources, and then working from the data to unlock business value and insight. Historically this was a ‘needle in a haystack’ approach, but now there are successful methodologies to ensure value realisation.
Components Of An Effective Data Strategy
There are four components of an effective data strategy. When all four components are properly constructed and aligned, they come together to provide accurate, reliable data that supports business objectives and offers real value.
1. Business strategy
Decide which questions you want the data to answer, and then explore which KPIs and metrics will help to reveal that answer.
2. Roles and governance
Within your organization, make sure you and your team are knowledgeable about the different responsibilities in regards to data. Know who is in charge of what data, what questions you can ask, and what types of reports are available.
3. Data management
The data you work from needs to be secure, clean and accurate. This happens best with a “privacy by design” approach in the collection and storage of data.
4. Technology selection
The tech you can use to collect and store data varies widely, so make sure you’re using something that suits your needs without going overboard. Understanding what types of outcomes you want can help you find the right tech solutions for your business. If you’re not sure which platform or software to choose, talk to your data engineers. They’ll be able to help.
Troubleshooting Your Data Process
In our work helping businesses answer their biggest questions about data, we often see that the biggest problem companies have is disorganisation. Even if you have all the components listed above and execute your strategy perfectly, it’s hard to find answers in a giant pile of disorganized data. Make sure your files are organized and documented to the nth degree.
Availability of data is another issue. Sometimes, you just can’t access the data you want. Maybe it’s locked down with privacy restrictions, just doesn’t exist, isn’t granular enough, or you can’t find the frequency you are looking for. There’s always a workaround. You may need to ask a slightly different question to get the right answer. Recreate the process with different data points.
The most painful, yet fixable, problem in the data process is a lack of competence in finding and translating the data. If you or your staff don’t feel comfortable doing data collection and analysis yourselves, hire an outside company to do it for you until you become proficient enough to do it yourself.
How To Use The Data You’ve Collected
You’ve gone through the process, worked with your data partners, ran in-house reports, and now it’s time to make sure your data supports the questions you set out to answer.
Take your data and use it to visualize answers to your original questions. Does it actually and accurately provide clear answers to your questions? If it does, then use the data to make data-driven decisions that propel your business forward.
If you’re not seeing clear answers from the data you collected, it may be time to go back through the process and see where your original data collection process went off the rails. Would there be a better origination point for data that would provide clearer answers? Is it possible that the data you collected came from out-of-date or unclean sources? Go back, figure out what went wrong, and try again.
Data Collection And Analysis Is Complex, But It’s Worth The Effort
Rather than making decisions based on guessing, trial and error, or bias, data allows you to make certain, confident decisions for your business. Relying on accurate data for decision-making can help you achieve better outcomes that help you scale intelligently.
Don’t let the complexity of the data collection and analysis process intimidate you. If you’ve ever wished for a partner that can help you make tough business decisions, data is it.