Data seems to be the word of the moment — everyone is talking about it. From companies asking for consent to use your data via cookie policies that pop up on their websites to your friends complaining about their phones “watching” their every move and “Google having all of their data”. And we can’t forget the myriad of online articles praising the latest data startups or explaining why data is the future.
And yet, despite all the buzz surrounding data, many people still have unanswered questions: What even is data? And why should we care about it?
Putting it plainly and simply, data is a collection of factual information (mostly in digital form nowadays) that can be stored, processed, shared, and used in calculations or to create analyses, draw conclusions, and understand performance. Basically, data is a means to an end, and it can be expressed in either a quantitative or qualitative way.
In a business context, data refers to all information that has to do with a company: from information about your employees, customers, product, and sales, to website and marketing performance. Qualitative data would be, for instance, an interview with a client to understand how satisfied they are with your product or any pain points they might have. On the other hand, quantitative data could be all the numbers generated from your website analytics or the insights you can obtain from your organic posts on LinkedIn.
Ultimately, data helps businesses make informed decisions to drive the company forward, improve efficiency, increase profits, and achieve organizational goals. On a lower scale, it enables employees from all backgrounds to work in a more effective and productive way, learning what performs well and what doesn’t, and allowing them to optimize processes and adjust workflows accordingly.
What’s more, high-performing companies report a significant increase in their revenue and earnings after implementing data and analytics (D&A) initiatives.
Data in and of itself won’t tell you much. Raw data can be disorganized and confusing, but once you’ve cleaned it and transformed it into meaningful tables and digestible reports, you’re one step closer to making sense of the mess.
Essentially, you can turn your data into a story, a collection of insights that shed light on the successes, failures, and progress of your organization. This can then serve as the basis for actionable plans and strategic business decisions.
Put in less abstract words, data can help you:
For instance, if your sales team hasn’t been able to nail down the right target audience, then customer data, information about your sales qualified leads (SQLs), and the insights from your paid marketing campaigns can help elucidate this matter.
With historical data, you can predict customers’ behavior, your monthly or annual recurring revenue, and even forecast the performance of sales at specific times of the year.
Data also allows you to prepare for potential new issues in advance.
In order to put data to use and gain the insights you are looking for, you need to analyze it. The type of analysis you implement will depend on the use case you are working on:
Descriptive Analysis: Descriptive data analysis looks at past data and demonstrates what happened. This is often used when tracking key performance indicators (KPIs), revenue, sales leads, and more.
Diagnostic Analysis: Diagnostic data analysis aims to determine why something happened. Once your descriptive analysis has shown you that either something positive or negative happened, diagnostic analysis can be used to figure out the reason behind this occurrence. For instance, a company may see that marketing leads increased during the month of October. They could then run a diagnostic analysis to determine which of their marketing efforts contributed the most to this boost.
Predictive Analysis: Predictive data analysis predicts what is likely to happen in the future. In this type of research, trends derived from past data are used to form predictions about the future. For example, to predict next year’s revenue, data from previous years needs to be analyzed. If revenue has gone up 20% every year for X number of years, you could predict that next year’s revenue will also be 20% higher. Predictive analysis can also be applied to more complicated issues such as risk assessment, sales forecasting, and lead qualification.
Prescriptive Analysis: Prescriptive data analysis combines the information obtained from the three previous types of data analysis and forms a plan of action for the organization to face issues or make decisions. This analysis poses the question of “what to do next?”, and enables companies to form data-driven answers.
Despite the countless advantages that data offers, less than 50% of corporate strategies talk about data and analytics as a crucial part of delivering value in their companies, according to the research and consulting company Gartner. However, Gartner expects this percentage to rise to 90% in 2022.
So what can you do to step out of the dark? How can your organization follow in the steps of companies like Big Tech, data masters appraised for their ability to collect and process large quantities of data, and then use it to continuously feed all of their business strategies?
Well, there are a few things you can do to become a data champion yourself:
Which KPIs are the most important in grasping the performance of your marketing campaigns or the growth of your company? At the end of the day, what you can’t measure can’t be improved, so it is crucial to know what you need to measure and how to do it properly.
This means data has to be accessible to everyone in the organization that needs it, and it shouldn’t be scattered around and blocked by specific stakeholders. A great solution for this issue is a data tool like Y42 which integrates all your data sources and allows you to access them all in one place.
Data literacy encompasses reading, analyzing, working, and using data to support arguments. Acquiring these skills is key to actually reap the benefits data has to offer. Workshops given by data experts could be a good way to start your data literacy journey.
Routine discussions of D&A strategies will bring you closer to wielding and operationalizing the power of data.
The key is to prevent data from becoming another buzzword you eventually get tired of hearing. Instead, allow data to be at the forefront of your business strategy, your day-to-day work, and the way you frame hypotheses and conduct experiments in your organization. No matter what department you find yourself in, data will always be waiting for you to make sense out of it and turn it into a valuable story that everyone across the board is interested in.
If you're ready to become a data champion, then don't wait any longer and contact us. We'd be more than happy to guide you in your data journey.