Data-driven decision-making allows businesses to use large information sets to see patterns, trends, and outliers. In the information age where data is king, this means businesses no longer need to rely on gut instinct, intuition, or history. With quality, relevant data, businesses can, instead, make well-informed decisions without any guesswork.
Below, we’ll explain what data-driven decision-making is and how it can help businesses of all types. We’ll detail some of the advantages to use a data-centric approach, and also why it’s important not to rule out the human element too.
As its name suggests, data-driven decision-making takes data collected in various ways and utilizes it to provide valuable insights. These insights can then be used to make important decisions with the backing of hard data.
Data is typically collated from historical trends and market research and converted into visual representations that make digesting the figures easier and observe patterns.
Visualizations such as bar charts and graphs also make it easier to present data to senior colleagues in a business if necessary.
Businesses will typically track Key Performance Indicators (KPIs) to assess the efficacy of data-driven decisions. These will be quantifiable metrics that can easily be compared. For example, comparing the number of ad clicks before putting data first to after.
Some other data-driven business decision examples include:
Using traffic data to decide on new premises.
User-feedback data to improve a prototype.
Use social media data to reduce marketing waste.
Harness marketing data to see how a product fares in a certain demographic.
Armed with information at their fingertips, businesses can therefore begin to make better decisions. This means more leads, more opportunities for growth, better process maturity, and efficiency.
The benefits of data-driven decision-making
1. Reduces risk
When a company uses data rather than personal opinion, historical precedent, and gut instinct, individual employees are able to justify their decisions with hard figures and information.
In using data analytics to derive insights, bias is all but removed from the equation and the business environment is much better understood. Businesses are then able to improve risk management, meeting demand while protecting themselves using risk simulations with data and locating threats.
Becoming data-centric also helps businesses become more efficient. Using data and KPIs, businesses can pinpoint actionable objectives. This means resources are not wasted on fruitless endeavors.
In using data, everyone within a company can rally behind an idea. Unlike subjective decisions, data-driven decisions are based on insights that are more apparent, meaning the company as a whole can feel confident moving forward with decisions.
Having a data-driven workplace means that everyone involved can have access to a wealth of information. Data can be used to inform critical decisions in a more timely manner.
For example, a website design team may receive information back from a user-experience team suggesting some button layouts are causing issues in testing with 75% of users taking longer than 5 seconds to find the login button. This data can be acted on instantly and incrementally, with the information used in future decision-making.
In removing some of the subjective elements of decision-making, employees can also feel emboldened to discover efficiencies and innovations of their own.
Without data to back-up ideas, employees can often be uneasy to speak up with ideas and suggesting new approaches. With the backing of hard data, however, companies that put data first are discovering that employees become more proactive and feel confident suggesting new ideas.
Both mid-sized and large companies can find a host of efficiencies using data. These can save money as well as time. This can be something as simple as ensuring stock and supplies are only ordered when necessary.
Data can equally be used to make de-risk multi-million dollar decisions basing quantifiable metrics to inform plans. This saves money by reducing making costly mistakes.
Data-driven decision-making also helps businesses keep in touch with what their customers want. Companies can make predictions about demographics and trends that could see demand drop or grow unexpectedly.
Feedback can also be used as data points, acting on both negative and positive trends to retain customers and improve reputation.
The disadvantages of data-driven decision-making
1. Data-driven decisions require interpretation
It’s important to note that while data is quantifiable and objective, wrong decisions can still be made. While data might indicate a certain outcome is likely, patterns can be deceiving.
Ultimately, data needs to be interpreted. Interpretation is open to suggestions, mistakes, and inaccurate observation. Any decisions based on such interpretations could be wrong, leading to costly mistakes.
It is sometimes the case that the data collected is of low quality and is not particularly useful. Data of this sort is harder to work with and will not help inform decisions. This can be frustrating to teams and highlights the importance of isolating exactly what type of data would be useful before collection.
A technical issue that can be encountered is that there is no set universal standard for data-driven decision-making.
Analysts will typically have multiple formats to import into software including XML, CSV, and JSON files. All of these will normally require conversion to work together, with conflicting issues and incompatibilities common.
A way to avoid this is to harness data management tools for both collection and formatting. This streamlines the process and makes data much more usable.
Discovering patterns and trends within mountains of figures is not easy. It requires creating visualizations of data to first make information human-readable. It then requires interpretation, with lots of additional factors held in mind while doing so.
While this is difficult, data analysis of this kind is a skill that can be learned. There are online courses, University courses, and evening courses all available to learn data-driven decision making with lots of workplaces now valuing the skill.
Some real-world data-driven examples
Amazon
One of the best-known examples of data-driven decision-making is Amazon’s recommendation engine. It now drives upwards of 29% of sales from the platform accurately recommending products to users based on key metrics such as past purchases, browsing habits, wishlists, and their current basket.
Using this data, Amazon’s decision systems effectively pick out items shoppers will like, leading to more sales.
Netflix
Another success story is Netflix. Creating the next big hit might be something of an art, but Netflix has successfully leveraged data to make winning shows, describing itself as “a data-driven company since inception.”
Netflix uses data to inform its content strategy. from over 30 million sessions, ratings, searches, and browsing data, Netflix went on to create hits like House of Cards and revive gems like Arrested Development.
Coca-Cola
With a 105 million strong army of Facebook followers and millions more on Instagram and Twitter, Coca-Cola used data science and social data mining to discover who was mentioning and consuming their products.
This data was then fed into its ad campaign and used to target ads more specifically at people who seem interested in Coca-Cola. And it worked, by simply observing patterns in data, ads were clicked four times as much as before, generating more revenue and brand recognition.
Creating a data-driven workplace
It’s not just the likes of Amazon and Netflix using data-driven decision-making though.
Moving towards data-driven decision-making is all part of the digital transformation that many sectors and industries are undergoing.
Both big and small businesses can harness the power of data and make better decisions.
Creating your own workplace or processes that put data first can be challenging at first, but ultimately extremely rewarding. In harnessing the insights of data analytics, businesses can speed-up decision-making, and discover innovations they wouldn’t have otherwise.
Here are some tips to make your workplace more data-driven:
Start small at first, focusing on simpler metrics to accomplish a single goal. For example, a visual of sales per month with bars representing age demographics. A company may discover that their plant-based product is three times more popular amongst 45-50 years olds in January. This can lead to some key marketing decisions being made on sound data.
Demonstrate data-driven decision-making yourself before expecting others to do the same. By taking the first steps, you can lead by example and discover potential decisions you can bring up during meetings and with senior colleagues. This can lead to a wider conversation about the role of data within the company.
Use learning resources to improve your own and others’ data interpretation skills. If possible, implement a workplace scheme to introduce the concepts of data-driven decision-making and offer resources for people to upskill. This can be in-house education, or funding classes both online and offline for employees to become more valuable.
Conclusion
While moving towards data-driven decision-making can be daunting at first, it is a proven way to improve a business. In moving towards becoming a data-centric workplace, companies can reduce risk, grow business, keep customers happy, and feel more confident in their goals.
