Why You Need to Take Data Science Into Actionable Intelligence

Data science has been a buzzword for years now. Many organizations have incorporated data science into their business practices, but only a few have taken it beyond the analytical stage and into actionable intelligence. This is because data science is no longer just about analyzing data for patterns or insights. It’s about creating intelligence out of that data to make better business decisions based on real-time facts instead of assumptions. To put it simply, data science as a practice needs to go beyond just crunching numbers and find ways to tap into its potential as actionable intelligence. After all, there’s no point in having all that information stored in your databases unless you can use it to take smart business decisions and pivot quickly if need be. Here are some reasons why you need to take data science into actionable intelligence:

Data Science is a Commodity

Analysts only look at data for insights, but marketers want to create ROI out of that data. This is because data science has become a commodity with most companies offering it as a service. There are now thousands of vendors offering data science services, which has led to its commoditization. In simpler terms, businesses are now able to buy data science services as a commodity and get their money’s worth. But what many businesses fail to realize is that these commodities don’t just stop at the level of analysis. There are vendors that offer data science as an end-to-end service, which means they analyze data, create models, and take actions based on the data. This means that the data science team is responsible for creating a strategy and managing it right through to its execution. But, not all data science teams are created equal. Not all of them will be able to bring actionable intelligence into your business practices.

Real-Time Action Is Key

Data science is all about analyzing data, looking for patterns, and then making predictions. What many people fail to realize is that data science is supposed to take a long-term approach to things. Data scientists have created models based on the data that they analyzed. They’ve also predicted how the business will perform in the future. This is a great thing if you’re already seeing a dip in your profits and you want to know how long it will take for the business to recover. But what about businesses that are just starting out? What about organizations that are in the midst of growth? In these cases, relying on long-term predictions just isn’t enough. There are many instances when a short-term approach to business decisions is more beneficial than a long-term approach. If a business is going through a growth phase, they need to make sure that they’re able to keep up with the pace and take quick action if something isn’t going according to plan. Data science alone doesn’t provide instant feedback on the effectiveness of your business decisions. This means that data science is more of a long-term plan that provides predictive analysis. This is why actionable intelligence is needed. It’s an approach that allows you to tap into real-time data and make instant decisions based on that data. The catch is that you need to be able to collect this data in the first place.

Data Literacy is Crucial

As mentioned above, data literacy is required if you want to take data science into actionable intelligence. This is because data literacy determines how effectively you can collect data, analyze data, and make decisions based on that data. Being data literate is crucial for any organization because it allows them to make informed decisions. Being data literate also helps organizations save valuable time and money because they don’t have to rely on assumptions or gut feelings when taking important business decisions. What many businesses fail to realize is that data literacy requires a different set of skills for each department. For example, accountants will need to be skilled in financial modeling, whereas marketers need data analytics skills, and IT departments will need data engineering skills. It’s important to have a team that’s data literate because they’ll be able to collect actionable data and take immediate action. This will allow the organization to improve its business performance and make better decisions based on real-time facts instead of long-term predictions.

Bias Error Detection

Bias could create huge challenges when it comes to collecting data and making decisions based on that data. Data scientists have trained models to recognize certain trends in the data. They also use algorithms to find causal relationships between different variables. This is helpful for most businesses that are looking to create models based on their data. However, there’s a chance that the model could be biased, which means that it’s feeding false results. This could happen if the model is trained on flawed data or if a particular algorithm is biased. Bias could cause the model to show inaccurate predictions and affect your business decisions in the process. This is where bias error detection comes in. It allows you to identify potential flaws in your model and correct them before they feed false results to the model. Bias error detection could be particularly useful during the training phase of your model.

AI and ML Are Here to Stay

Many people think that data science will eventually evolve into a field that relies on artificial intelligence (AI) and machine learning (ML). These are two cutting-edge technologies that are already being used in various industries and will only continue to grow in popularity. The data science field is expected to grow by a staggering 11% annually from 2019 onwards, which means that there will be an increasing demand for data science professionals. Unfortunately, there are only a few people who are skilled in data science and know how to take it into actionable intelligence. That’s because most people who know how to perform data science only know how to do the analytical part. This means that they’re not familiar with the implementation part.

Bottom line

The bottom line is that data science has to go beyond just analysis and take actionable steps towards becoming actionable intelligence. Data scientists need to be able to collect real-time data, analyze it, and make instant decisions based on that data. This can be done by using bias error detection and AI/ML to train models.