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DATA & ANALYTICS BLOG

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Bringing Data Science in House: 4 Components you need to be Successful

Posted by LPA Software Solutions on Sep 2, 2020 5:33:58 PM

Most businesses have realized the importance of data science in the current business environment. Thanks to the Internet of Things, the sheer volume of data available for organizations has increased exponentially and will continue to do so for the foreseeable future.

But businesses without data science capabilities often ask the same question: what do we need to bring data science in-house? While data science is by no means new, the journey to understanding data science and AI is still in its infancy in most organizations.

Learn more about what you need to have a successful data science department in your business.

Data science (DS) and Artificial Intelligence (AI) are two leading technologies applied when processing data to drive business operations and decision making.

The Four Components of Data Science

Of course, these systems interpret large amounts of data to derive patterns/trends and make predictions – this drives their ‘decisions’. This is how AI relates to DS. The latter applies deep learning, machine learning, and other aspects of AI and data and statistical analysis.

If you want data science capabilities within your business, you should answer two questions:

  • Why do I need data science?
  • Can I tackle it in-house?
Portrait of technician working on laptop in server room
There are specific prerequisites that should be fulfilled to run the high-level data processing and business intelligence needed for in-house data science. They include: 
  1. Use case – the business problem you need DS to solve
  2. Data – some data relevant to the business problem
  3. Data scientist – the professional who interprets the use case and manipulates the data
  4. Data science tool – AI, data visualization, and statistical tool

These are discussed in detail as follows:

1. You Need a Business Problem

Big data in organizations helps to provide insight or solve business problems. The business problem is the challenge the business faces and wishes to resolve using data. Typically,  executives will meet with a data scientist whose job is to:

  • Translate the problem into a process flow
  • Determine the data required to shed light on the problem
  • Determine the type of AI and data science techniques to use
  • Perform data modeling, thought experiments and other methods until he/she has useful insights
  • Interpret the data insight back to line executives through data visualization techniques

For data science and analysis to be effective, the problem must be well described, including all possible business questions. You should also outline any auxiliary requirements, e.g., increasing cross-selling opportunities without losing the customer. Specify expected outcomes in business terms, e.g., increasing website conversions by 20% in 3-6 months.

Below are some examples of use cases or business problems where data science would be useful:

  • Detecting risk factors for insurance underwriting
  • Improving forecasting for utility budgeting and capital planning
  • Detecting hidden defectors in retail banking
  • Improving marketing reach and increasing ticket sales or visitors in entertainment
  • Determining slow-moving consumer product or those at-risk of decline
  • Automating customer interactions to decrease costs while increasing customer satisfaction

You will need to determine what data is useful to study the business problem or use case presented. If you store your data in-house, the data warehousing team's input and business intelligence expertise will be needed. Finally, it helps to consider the actions you will take once the analysis is complete.

2. You Need Data

Data is the key to data science, the material that informs any statistical analysis or business intelligence. In data science, data is divided into traditional and big data.

Traditional data is collected actively by all businesses, and it is often structured and stored tables in relational databases or a data warehouse. Conversely, big data is a massive collection of information collected both actively and passively. It is varied, including structured and unstructured data, and churned out at a high velocity.

Big data may or may not be subjected to further processing before storage in a process called data warehousing, championed by the data architect and/or data engineer. The data scientist finds and uses what they need from the storage location. He/she may also need to purchase/find more data outside the business.

3. You Need a Data Scientist

Technician fixing computer hardware

The professional in charge of the entire process of defining the use case to deriving insights from data is a data scientist. Many data scientists have a statistics or computer science foundation with a Master’s degree in Data Science. They may also be other data professionals who have taken a certification course in data science.

A data scientist likely has background knowledge in programming languages like Java and Python, and platforms like Pig and Hadoop. They understand business analysis and analytics modeling, and they may specialize further to cover specific niches in data science.

They should also have professional skills like collaboration, communication, leadership, creativity, problem-solving, discipline, and the drive to find truth in data. Having strong interpersonal skills are crucial for a data scientist.

4. You Need a Data Science Tool

The best data science tools provide collaborative environments, allow governing of data and assets, and enable deployment of data models built in the tool. Business impact can only be felt once the data models are deployed. The tool should have multiple modeling interfaces to support a variety of data skill sets so that all your data professionals can work from the same platform.

Cloud Pak and Watson Studio are two tools you can use for data science. Watson Studio is an AI tool with the above capabilities, and it sits on Cloud Pak, which is a platform where service licenses can be built up and down according to your business needs.

What Do You Need to Set-Up Data Science In-House?

Now you know all components needed to run a successful in-house data initiative. If your only gap is the data scientist, LPA can do your data science work.

If you have data professionals who are not yet data scientists – data analysts, business intelligence professionals, data engineers, and architects, etc., we can also train them to do data science work. Alternatively, we can help while you source your in-house data resources, e.g., hiring a data scientist.

Talk to us today, and let is help you develop a data science department in your company. Data is the future, and the future is now.

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Topics: Data Science & AI