Is It Time To Hire A Data Scientist?

Data and analytics have taken the business world by storm – finally! It’s been a long time coming though many may say they’ve always used data to support decision making. While true for some, the rise of Data Science from the back office to the boardroom conversations as the most in-demand position is relatively new. From finance and manufacturing to marketing and operations, data science has extended its hand to have application in almost all business aspects.

But what makes this role in such high demand? If you’re contemplating whether or not to build an in-house team, we’ll shed some light on the role Data Scientists play and considerations you’ll want to understand before making a decision.

Roles of a Data Scientist

The primary role of a Data Scientist is to extract, organize, and analyze data. Using software tools, mathematics and statistics these individuals are able to prepare sometimes immense amounts of data. Their objective is to convert it into actionable information that can help the leadership make critical business decisions. Thus it’s expected they’ll have a very good grasp of related technologies and trends in data storage, management and research. Senior Data Scientists are often involved in supporting C-level executives by providing insights into processes and methodologies that can turn raw data into key strategic assets of the company. This is where experience in the business domain of the hiring firm is extremely helpful.

Types of data handled by a Data Scientist

As we’ve seen, the main responsibility is to analyze data. Ideally, the process begins with them collecting data and ends with a recommendation on business decisions you should make based on the analysis of said data. Typically, they’d collect this data from different sources and in different formats. The first one is in the form of structured data.

Structured data includes data collected in the form of numbers, dates, text strings, and other formal data that is typically easy to classify, organize and store in a database. Types of important data that fall into this category include website analytics data, bank transactions, sales figures, inventory, schedules, directories/lists, and data from company operations. Structured data is more organized and generally easier to manage, use and analyze. Relational database management systems (RDBMS) typically serve as the repository for structured data.

The other major type of data happens to be the fastest-growing source of data. Unstructured data is the “everything else” of data and includes formats like audio, video, text files, email logs, social media, image data, digitized communications (i.e. chat, IM, call recordings), and application data plus other forms that are generally more challenging to analyze. Unstructured data represents 80% of the world’s data according to research firm ITC.

Can’t Someone in IT Handle It?

Perhaps, if that individual has the requisite skills as previously mentioned to turn raw data into company assets that drive strategy and decision making. More challenging is the ability to seamlessly move between working with structured, unstructured and mixed data.

Not that long ago companies would have someone who knew digital marketing. One individual might manage the website, analyze traffic data, build site features, apply search engine optimization, run digital media campaigns, and send out the monthly company newsletter. As we are all aware now, specialties have evolved as the individual tasks have become more complex and crucial for company success. So too is the challenge with data. Specialists have developed because data is now known to be a critical asset. These specialists understand data strategy, storage options (i.e. data warehouses, lakes, marts, etc.), access and use governance, ETL considerations and more that make the job far more complex than software, hardware and networks.

The Data Scientist’s Counterpart

Data scientists generally operate best in a team. Such teams typically include a Data Scientist or Data Engineer and a Data/Analytics Translator. We’ve already gone over what the Data Scientist/Engineer can do, but the Data Translator has a different role.

Data Translators are neither engineers nor architects. They can sometimes be called Business or Decision Analysts. They may not even have deep technical expertise in modeling or programming. Instead, the translator works to bridge the technical team with the operational and functional teams. Data translators are crucial because they ensure that the analytic insights are interpreted correctly and the potential impact of decisions understood.

The two actively work together to gather requirements, define problems, and actively project outcomes in relation to business goals. It’s not unheard of for Data Scientists with deep domain knowledge to be effective in the Data Translator role, but it is less common and these individuals can command premium salaries.

Where Are Data Scientists Skills Most Frequently Applied?

Perhaps the biggest forte of data scientists is that they can work in a variety of industries. For instance, in business, they play a major part in making decisions on inventory, efficiency, customer satisfaction, and production errors. In e-commerce or digital marketing, website and visitor behavioral data is collected and analyzed with the intent of recommending improvements to conversion rates, lead generation or latest effective strategies and tactics in digital or social media.

Then there is finance, where they analyze information on risk analysis, revenue/expense projections, customer analytics, AR/AP, fraud detection, security and compliance. In this case, a Data Scientist’s expertise can be extremely valuable in providing supporting analysis for an organization’s financial governance and policies.

Manufacturing and service operations are growing in their adoption of analytics. Particularly areas like predictive maintenance of machinery or customer service where shortcomings can have negative consequences. More recently Data Scientists are finding their way in environment and sustainability industries and health care, where they discover trends and interpret medical records to improve health services.

Conclusion

As the current saying goes, “data is the new oil.” The value of data science properly applied within an organization can translate directly into competitive advantages. This makes effective management and use of data critical for all organizations. Having said that, the elephant in the room needs to be acknowledged. Quality data science talent is hard to comeby and expensive. According to Glassdoor and Payscale, the average salary for a mid-level Data Scientist is about $119,000 as of August 2021. This makes finding and onboarding data science talent a risky proposition for smaller companies. Alternatives exist that are more appropriate for firms starting their exploration of leveraging data to gain insights. Speciality consulting firms can offer the services of a full data science team on a project or as-needed basis. For many companies “renting” before buying is an option worthy of consideration. Whichever option is chosen, do so knowing it’s an investment in building a better operating system for your company that will only gain value over time as data becomes increasingly more important.