Good decision-making is key to companies and institutions running efficiently and overcoming unforeseeable obstacles. With the help of data science companies, decision makers are now able to make better-informed choices than in the past by shaping and filtering the data their organizations have collected. By using this data, they are able to formulate predictions of the future based on what would happen if they decided to take their organization in a brand new direction, for example, or how they would rebuild themselves after a financial disaster, among a variety of other things.
It should be mentioned, however, that having data science alone is not always enough. As Irina Peregud, of InData Labs, explains: “Data scientists analyze data to find insights but it’s the job of product managers and business leaders to tell them what to look for.” Essentially, business leaders and heads of governmental institutions need to know what the problem is before they send in their data science troops to try and solve it. Data scientists are able to dig up masses of information but it’s worth nothing unless they are led by someone who understands the setting in which they are working — a leader with industry experience. Goals need to be clearly set before data scientists are able to theorize ways to reach them.
Building automated response systems is often seen as an end goal for many business leaders seeking to invest in data science. Many small decisions can be automated with ease when the right data is collected and utilized. For example, many banks that grant loans have for many years now been using credit scoring systems to predict their clients ‘credit-worthiness’, however, now, with the aid of data science, they are able to do this with a much higher degree of accuracy, which has relieved their employees of some the decision-making process, lowered the possibility of not getting a return on their loans if the customer was not ‘worthy’, and also sped up the process as well.
On top of that, data science is also able to help automate much more complicated decision-making processes, with the ability to provide numerous solid directions to choose from with data as evidence for those possibilities. Using data science, it is possible to forecast the impacts of decisions that are yet to even be made. There are many examples of this, but perhaps some of the best known are of companies on the brink of collapse that placed their trust in data science and were saved by remodeling their company based on what the data told them would work, such as Dunkin’ Donuts and Timberland. The former, invested in a loyalty system and the latter invested in identifying it’s ideal customer. Having the data to back big decisions such as these, allows decision makers to feel more confident in what they are doing and invest more in the idea financially as well as psychologically.
Healthcare and Insurance
Healthcare is another where area data science has shown to be highly beneficial for decision-making in a variety of sectors. Obviously providing adequate treatment is the number one priority. Many healthcare providers now are moving towards evidence-based medicine, which when used in conjunction with data science, enables physicians to provide patients with a more personalized experience by accessing a larger pool of sources before making a decision on treatment.
Health and life insurance are other areas of healthcare that benefit significantly from data science. Similar to how banks grant loans based on a ‘credit-worthiness’ score as mentioned above, health and life insurers are able to develop ‘well-being’ scores. To develop such a score can involve the collection of data from a large number of places, including social media, financial transactions, and even body sensors. This can also be seen throughout the insurance industry as a whole. In fact, as Datafloq explains: “Insurance companies rely on growing their number of customers by adapting insurance policies for each individual customer”. By using data science, insurers are able to grant more personalized insurance schemes that work for both the customer and the insurer. This makes the decision-making process easier as it is no longer a question of insurers saying ‘yes’ or ‘no’ to customers, but about questioning the terms that will work for both parties involved.
Data science is also well-known to those that aim to improve operational standards, too. By applying data science to operational procedures, decision makers are able to implement changes much more efficiently and monitor if they are successful or not much more closely through trial and error. Such methods can be applied to hiring and firing employees by collecting and measuring data to see who fits the job best, as well as measuring performance targets to see who really deserves to get promoted. On top of this, it helps employers see where work is really needed and where it can be cut.
William Edwards Deming once said: “In God we trust. All others must bring data.” Though he passed away more than 24 years ago, his words now hold more truth today than when they were spoken. With the aid of data science, decision makers — whatever industry they may be in — can make much more precise choices than ever before. Or, in some situations, wipe out the whole decision-making process by automating it.
By harnessing data science to its full potential, top-ranking decision makers in all industries, not only make better-informed decisions but make them with clearer predictions of the future. With that advantage on their side, they are able to stabilize businesses that have not always had a clear vision and save businesses that are on the brink of collapse.
However, it should be mentioned again that data science is only an advantage when decision makers know there is a problem to be solved and can give the data scientists under their leadership goals to aim for. Once goals have been established, data scientists can work their magic and theorize how to fix it. Data science alone is not an advantage for decision-making, data science combined with good leadership is.