Whether you’re a business owner trying to make your organization more efficient or an individual trying to climb the ranks within a business, you’ll need to know how to work with data to make real progress. In today’s highly competitive world, intuition is not enough. One of the first things that developing your data analysis skills will make clear to you is that it’s very likely that those who think they got to the top through intuition were simply lucky or were taking advantage of the data-driven insights of others. Leadership may be about instinct and inspiration, but management needs to be rooted in a solid understanding of probabilities and facts. That’s why data-driven decision-making is now widely considered to be essential.
What is data-driven decision making?
Data-driven decision making (DDDM) involves designing studies, conducting research, collating the data produced by that research, and analyzing it to identify useful information, then applying that information in the decision-making process. Not everyone who works with DDDM is engaged in every part of this process. In many cases, data analysts work with data that has already been collected by another individual, department, or organization, but the information they extract from any given data set may differ from that extracted by others. This is because there are often multiple patterns to be found within a data set — multiple ways of comparing, contrasting, and identifying relationships between particular data points. As more businesses work with big data sets, the analysts exploring them have more options. This is getting easier to do as computers are getting more powerful and thus more capable of processing large quantities of data at once.
Overcoming natural bias
While analyzing data like this may sound challenging, it’s often quite simple, and it’s much more reliable than the alternative. Although intuition can be fantastically helpful in some situations — such as when you’re asked a question by a journalist and don’t have the chance to check the data before replying — it has limitations even for the smartest people. That’s because our minds have inbuilt biases. We’re so good at pattern recognition that sometimes we spot patterns that aren’t really there, and if we don’t check, that can get us into trouble. We also make unconscious assumptions based on experience or things we were taught during our formative years, which may seem like common sense to the point where we don’t even realize we’re relying on them yet actually lead us astray. We also get tired. Multiple studies have shown that we’re much better at making decisions early in the day, and that the more decisions we have to make, the worse we get at them — a phenomenon known as decision fatigue.
Why not all data is equal
Is it still possible to make bad decisions when basing them on data? Yes, because data quality varies. That’s why data analysts, even when they don’t design the original research, always spend time looking at it and assessing its structural integrity and objectivity. Even if the research is well designed, the degree of confidence that can be placed in it depends on the sample size — or, more accurately, the size of the sample in comparison to the population (of people or things) that it represents. Larger samples in comparison to population are more likely to properly reflect the diversity of that population if they have been randomly sampled. Sometimes random sampling is not possible and where, say, ambiguous questions have been asked, data can still be useful, but the risk of inaccuracy is higher. It’s a good idea to keep looking out for better data.
Improving your data analysis skills
The best way to get to grips with all this is to take a course that will help you understand how to work with data sets and how to use what you retrieve from them in the context of business. The Aston business analytics course is online and part-time so comparatively easy to fit around your existing workload, plus it offers the one-to one-support you will find invaluable if you run into difficulty. You may also find it useful to download statistics software such as SPSS (widely used) or R (free from the open-source community) so you can begin to get to grips with it straight away, working on small data sets of your own creation and gradually exploring what it has to offer. While you will find this easier if you start from an analytical background, it is something that anyone can learn with sufficient application and patience.
How businesses incorporate DDDM
There are many different situations in which data analysis is useful in business, and research shows that the routine use of DDDM gives businesses a significant advantage over competitors that do not engage with it. This means that there are ongoing opportunities for people with a good understanding of data to find employment across a variety of fields. Although most data analysts tend to specialize over time, the core skills remain the same no matter what area you’re working in, so this is a very flexible career path with lots of room to move sideways. From the employer’s perspective, this is a big advantage because analysts can be moved around to meet the changing needs of an evolving organization. In smaller businesses, generalists are particularly valuable and are more likely to need to work on their own initiative, identifying areas where they can usefully apply their skills rather than simply being directed to them.
Understanding the market
When it comes to anticipating market trends, in the absence of insider knowledge, data analysis is simply the best tool available. Although they can never be guaranteed, predictions based on accumulated data are far more reliable than anything else. DDDM can also be applied in profiling and mapping the behavior of specific organizations be they competitors, suppliers, customers, or possible partners. You can even use it to get a clearer view of how your own business is faring and how it compares to others trading within the same sector. Every sizable finance department relies on data analysis to inform decisions, and bringing it in-house not only saves money but enables businesses to make informed decisions more rapidly during times when external advisers are not available.
Understanding customers
One of the things people are most reluctant to accept about data analysis is that it can help them understand people better than through personal experience. Granted, if we’re talking about a small business, such as a neighborhood barbershop where the proprietor knows all the customers personally and has spent years getting to know them, data probably can’t add much, but in larger organizations, it is essential. It helps identify new markets and clarifies the needs and desires of existing ones so that everything from products and services to the buying process and communications can be streamlined accordingly. Switching to a DDDM approach is frequently a feature of rescue efforts when companies are in trouble. Business owners, like anyone else, are vulnerable to being blinded by sentiment or getting set in their ways and losing control in a world that is constantly changing.
Streamlining production
Once any given production process has reached a certain level of efficiency, it becomes very difficult to tell, through simple observation, where further improvements can be made. This doesn’t mean that it’s not possible to make it more efficient, simply that better tools are required to work out how, and that’s where DDDM comes in. Crunching the numbers that stem from different parts of the process can produce insights that simply weren’t visible before, especially in large organizations where there’s a great deal to keep track of. It’s particularly useful for finding correlations between apparently distant aspects of the process. For instance, a particular supplier might provide parts that are harder to fit into packaging, which would slow down the packing process. Suppliers and packaging are usually dealt with separately, and because the difference per item might only be a second or two, an individual manager might fail to identify a problem that is costing a lot of money.
DDDM and HR
We all like to think that our decisions about hiring and promotion are based purely on sourcing the best talent, identifying the people who best fit the organization, and tracking performance. Sadly, research tells us that this is not the case. Most opportunities are still given to white men, and minority groups lag far behind. This isn’t just a problem for those individuals whose efforts and abilities go unrecognized — it’s a problem for whole organizations. We can see from studies that boardroom diversity is positively correlated with overall performance. While there could be additional reasons for this, having a diversity of perspectives is often useful in decision making, even when it’s based on hard data, it’s possible that businesses with more diversity are more genuinely focused on talent. Subconscious bias can be hard to spot, but by adjusting hiring practices to reflect what big data says about the makeup of a business, it’s possible to work around it.
DDDM and negotiation
Whether you’re discussing a deal with another business or trying to persuade a bank to lend you money, it’s much easier if you can back up your arguments with data. Demonstrating that DDDM is embedded in your business culture shows that you’re serious about what you do, and you understand the environment you are operating in. Financial institutions, in particular, love to see the numbers, but a report commissioned from somebody else will never be as convincing as a demonstration that you and your colleagues have the ability to collate and interpret the data yourselves. You are showing that you have a clear understanding of how to operate effectively and have the means to put that understanding into practice, so you will succeed regardless of whether you have help.
The value of confidence
There’s another way in which DDDM helps businesses, aside from its direct effects, and that’s psychological. When you know that you’re making better decisions, you’ll feel more confident, and when you’re more confident, other people will become more confident about working with you. This doesn’t just affect the internal workings of the organization or face-to-face meetings with others. It’s an impression created by the behavior of the business itself, and at that level, it changes the way you look to investors. Confidence emerges from positive experiences, and using DDDM reduces the number of unfortunate surprises. Even if times are tough for your business, you can keep disappointment to a minimum and avoid feeling that you’re not in control. This helps to sustain morale and reassure workers at every level that, in due course, the business will be in a strong position again.
For all these reasons and more, DDDM is an essential approach in business. It can be challenging to implement if it goes against an entrenched workplace culture, but workers usually come around to it quickly when they start seeing the results. It’s not a replacement for traditional leadership, which still matters. It’s just an improved way of informing that leadership and ensuring that the day-to-day management of the organization runs as smoothly as possible. Learning more about DDDM will improve your ability to operate strategically and take advantage of opportunities, enabling your organization to flourish and attain its true potential. It will also make you much more appealing to potential employers who will want to find out what your skills can offer them.
