In the movies, it always seems to be the people who follow their ‘gut instinct’ who come out on top. Some people do seem to have an instinct for business and that intuition can be valuable, especially at the start-up or small business stage. Even at that point though, you wouldn’t plow your valuable time and resources into an idea without thorough market research and other detailed analysis. In the modern era, we have access to more data than ever before, and smart and successful businesses make full use of it.
In some ways, it can seem we have too much data to deal with. In an ever more connected and digitized world, there are floods of data accessible to us. It can be difficult to know where to start and how to use all of this data effectively. If you want to do so, it’s important to educate yourself on how to find, analyze, and utilize these constant streams of data. In a 2019 survey that asked CEOs about the factors that “impeded their ability to leverage data to make more informed decisions”, more than half (54%) cited a lack of data-driven skills and analytical talent.
Resources such as an online MBA Business Intelligence course can help you learn how to manage various data resources, analyze data and visualize the results to improve performance management, optimize customer relations, monitor business activity, and support your overall decision making. These can be vital skills for business owners but a course in Business Intelligence can also provide a springboard into career paths such as data analyst, business analyst, and business consultant in a wide range of sectors. Businesses of all types can make use of the skilled and knowledgeable professionals that online MBA Business Intelligence courses produce to analyze data more effectively and make better data-driven decisions.
Intuition versus data
Economics Nobel laureate Daniel Kahneman, who won his Nobel Prize for work on human judgment and decision-making, proposed that we have two systems of thought: System 1, which is fast and intuitive; and System 2, which is based on deliberate reasoning. The first system does have its uses and could be valuable in helping people spot threats or recognize opportunities, for example. It is also prone to error, however. The slower and more methodical system of thought is less prone to producing poor decisions as it relies on critical thinking and analysis.
Another issue that data-driven decision-making can help guard against is bias. Many of our mental processes are unconscious and biases can influence the way we make decisions, whether we are aware of them or not. This can be particularly relevant when relying on intuition, although data alone is not infallible. Datasets may include certain biases, and it has been shown recently that even artificial intelligence (AI) algorithms can develop biases that may reflect those of the people that created them and the data on which they are trained.
Data can still go a long way to eliminating biases though, especially when you are aware of the possibility of bias and take steps to combat it, such as engaging in collaborative practices and democratizing data. The results can be tangible — one older but still relevant study of more than a thousand major business investments found that when organizations worked at reducing the effect of bias in their decision-making processes, they were able to improve their returns by as much as 7%. Data collection and analysis cannot always remove bias entirely, but it can help to drastically reduce its effect.
Intuition, or gut instinct, may still be needed in making a final decision. After crunching all the data, for example, a decision one way or the other may remain finely balanced with a neat row of pros and cons on either side. The point is, though, that decision-making should always be guided, informed, and backed up by as much data and rational analysis as possible.
What is data-driven decision-making (DDDM)?
Data-driven decision-making, often abbreviated to DDDM and sometimes referred to as information-driven decision-making, can be a complex process, but it is a very simple premise. Essentially, DDDM just means putting data at the heart of everything you do, using it to inform your decision-making and validate a course of action before you commit any more valuable time and resources to it.
That’s the basic idea, but how you collect and use data can take many different forms, as can the data itself. A simple definition of data from the Cambridge English Dictionary is: “Information, especially facts or numbers, collected to be examined and considered and used to help decision-making, or information in an electronic form that can be stored and used by a computer.”
This gives an incredibly broad range of potentially valuable data, including:
- Historical information — What do your company’s previous sales and results suggest in terms of what works and what doesn’t moving forward?
- Customer feedback and surveys — What products, services, and features are people looking for? What do they like and not like about your offering?
- Competitor data — What is working for others working in your field or sector?
- Tests and trials — Have you launched a new product or service in a test market, or conducted limited user testing, prior to a full release to gain valuable insights?
- Demographic data — How do statistical data relating to the population and particular groups within it apply to your services? Do demographic shifts suggest upcoming business opportunities or threats?
Those are just a few of the potential types of data that can be used. On average, every human created 1.7MB of digital data every second in 2020, leading to a staggering 2.5 quintillion total bytes of new data per day. Just for context, there are 18 zeros in a quintillion — and that’s being created every single day! Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing software (and certainly by the unaided human brain). There is no shortage of traditional data sets to work with as well though, and every business can successfully use data to improve almost every aspect of their operations.
Examples of data-driven decision-making
Many leading enterprises and household name brands use data-driven decisions at the heart of their businesses. Here are some examples:
- ‘People analytics’ at Google
Google is a technology company that prides itself on its people skills and, in particular, a program or approach referred to as ‘people analytics’. One often-cited example of this people analytics is Project Oxygen, which saw the company mine data from more than 10,000 performance reviews, along with surveys and other data sources, and compare what it found with data on employee retention. Data-driven insights into what employees valued and the traits found in high-performing managers were then used to create manager training schemes, resulting in a 5% increase in favorability scores for managers. Insights also influenced personnel decisions such as extending maternity leave to cut new mother attrition rates by half.
- Coca-Cola targeted ads
While Coca-Cola has enjoyed a series of successful marketing campaigns in traditional media over the years (even our current view of Santa Claus is heavily influenced by Coke), digital marketing is becoming increasingly more important and often requires a different approach. The company has used a combination of image recognition technology and data analytics to gain insights into the individuals drinking their products, resulting in a four times greater click-through rate than other targeted advertising.
- DBS bank transforms itself with data training
As one of Singapore’s leading banks, DBS has invested heavily in business technology and innovation in a bid to stay a step ahead of newer fintech offerings and challenger banks. This includes AI and data-driven services allowing them to offer intelligent banking capabilities such as investment proposals, stock recommendations, and FX rates, transforming the brand into something between a bank and financial advisor. To make this transformation, they trained more than 16,000 employees in working with big data and data analytics to identify opportunities, address challenges, and improve the experience for their customers.
How to use data effectively in your business
Not every business has the resources of Google, Coca-Cola, or a well-known international bank such as DBS, but every business can benefit from the effective collection and analysis of data.
Here are some important steps to use data more effectively in your business:
- Identify your objectives and priorities
You need to have a good idea of your business objectives and goals. It’s all very well to say that data should inform everything — and it should — but moving towards a data-driven model can take time, and you should start by prioritizing key areas and goals. These could be as loosely defined as ‘raise brand awareness’ or as specific as hitting a certain target for website traffic.
- Find the relevant data sources
‘Relevant’ is the keyword here, as there is so much data that it’s easy to get lost in it. You could find relevant data from many sources, such as customer feedback, business intelligence tools, social listening tools, and website analytics. Seek input from teams across the organization to help pinpoint these sources and identify the relevant data.
- Explore and visualize the data
Visualizing what is actually contained in the data is very important and tools such as graphs, charts, and maps can provide useful and accessible ways of doing this. Being able to effectively communicate what you find within datasets is key to making improvements within the organization and successfully leveraging the data.
- Develop insights from the data
With the right skills, you can use this data and accompanying visualizations to create actionable insights. These could vary enormously depending on the data and the area of business to which it pertains. Did historical data indicate where a product launch went right or wrong? Has website analytics identified a particular channel to focus on? Or has an analysis of demographics highlighted a missed opportunity?
- Deliver, measure, and repeat
Act on those insights and measure how well they work (analyzing the new data and keeping the ball rolling). Not everything tried will have a major immediate impact but, by transitioning to a data-driven model of business, you are less likely to make serious errors — and more likely to pinpoint where you are going right and wrong.