Do you really need Data Science? 4 Questions you need to ask before you decide

Data science is very popular, but do you really need it? It may not be necessary for your organization.

In order to answer this important question, it is first necessary to understand the purpose in the first place. Also, what kind of organization is suitable for using it properly?

Purpose of data science

There are two distinct but very important objectives. The first is to improve the products that your customers will use. Its outputs are called data products. The objective is to improve the decisions that your business will make, it is called Decision Science.

Data products use data science and engineering to improve product performance. For example, search, recommendation, decision making automation, etc.

Decision Science uses data to analyze business metrics. These include growth, engagement, profitability, user feedback, etc. Businesses can gain the insights needed for strategy and key decisions.

Data science for decision making

Decision Science supports business and product decisions by analyzing and visualizing data. Decision makers are everywhere in the organization. Product managers make decisions to prioritize product roadmaps, while executives make strategic decisions for their business.

The issues that decision science needs to solve are often difficult. They are often new issues that have never been experienced before. So data scientists have to deal with unknown variables and missing information. Also, such problems are complex, and many elements are intertwining, so it is difficult to understand the underlying causal relationships. Still, decision science issues can be measured. Also, the impact of this type of work on the business is significant.

You may feel that decision science is like data analytics. In fact, the difference between these two is unclear. Still, decision science is more than making reports and dashboards. Data scientists should not be doing jobs that can be done with BI tools.

Decision science and data products need similar skills. However, there are very few data scientists who are good in these two areas at the same time.
Decision science requires an understanding of business and products, system thinking, and high communication skills. Data products require machine learning knowledge and production engineering skills.

For small data science teams, you will need to either diversify your existing resources or expand your team. However, hiring and nurturing people specializing in each area is important for scaling the team.

Four questions to ask if you feel like you need data science

The following four questions should be answered before creating a data science team:

1. Can you commit to using data science to support decisions or create data products?

If you can not commit to either of these, you should not hire a data scientist.

Indeed, they can support strategic decisions. The precondition is that you should be committed to creating a culture of data. Basically, you need to commit to making decisions using data in your organization.

You may not need them at first. However, it takes time to hire the necessary talent. It also takes time for them to understand your data and your business. Without such an understanding, data scientists help you with decision making.

2. Can you collect the necessary data and act on the results of data analysis?

Engineers can create MVP (Minimum Viable Product) products based on a few products and design guidance. Data science, on the other hand, needs data.

Now, data is something that needs to be monitored and scaled. In order to build a recommendation system, the product needs to monitor user behavior. To optimize business decisions, you need important measures and detailed indicators of the outcome.

Data should tell you about product changes and drive the organization’s key performance indicators (KPIs). Data-driven decision making requires a top-down commitment starting with the CEO.

3. Are there enough useful features in the data to get meaningful insights?

Many people think that big data and data science are the same thing. The latter focuses on identifying the features from the noise that occupies most of the data. The amount of useful features is not proportional to the amount of data. The ratio of useful features to noise is important.

For example, ad products may collect data on events with tens of millions of impressions. But the feature here is the rare event that a user clicks on an ad. No matter how large the data is, the amount of signal is small. This fact will not change even if you can read the data.

4. Do you want to make data science your core competency or do you prefer to outsource it?

Creating a data science team can be difficult and expensive. If you can outsource and save funds, you should do so. One way is to use a consultant. An even better way would be to use cloud services for your domain. These services can create models as data is passed. You can also use them to automate actions and create reports. These solutions may not be perfect for your business needs, but you may need to make such a compromise to keep your business growing at the initial stages. That’s especially true if you can focus on what your core team can deliver more value.

So when should data science be a core competency for your business?

If data science solves an important issue for your business success, you should not outsource it. In such a case, it is more important to develop talent within your company to handle these tasks. Also, packaged solutions are inflexible. If your business takes a unique approach to solve problems, for example, if you are collecting new types of data or using analysis results in an unprecedented manner, then packaging Services may not be flexible.