As technology-driven companies scale, gathering and storing their data starts becoming more difficult. Using it to derive useful insights to make your processes more efficient becomes impossible without access to the right systems and talent.

High-growth companies often have to compete with legacy players with deeper pockets and better economies of scale. In such a scenario, intelligent use of data can often level the playing field – or even tilt it in favor of the underdog. To be competitive, these companies need to collect data and build a system to translate it into business value.

The process of using data in business
Companies need to have a solid operating model to collect data and convert it into insights. These insights can help businesses reduce waste, make their marketing more effective, and even predict patterns to make operations more efficient. Doing this consistently requires a systematic approach.

The raw data a company collects (transactions, interactions with users on their websites or platforms, etc.) have to be organized and stored safely, in what is called a ‘data warehouse’. Once that is done, the full process can be divided into four parts –

• Data extraction – Data extraction refers to the creation of code to extract data from the warehouse
• Refining and visualization – Building an analytics layer to aggregate underlying data, making it accessible and useful
• Data Modeling and projections – Constructing complex data models to predict responses in different scenarios
• Data interpretation and analysis – Aggregated use of predictive, descriptive, and prescriptive analytics to derive insights for guiding your strategy, finance, marketing and operations.

Each of these processes represents a different skill. Companies looking to set up this kind of system will need to hire 4
different specialists.

1.  Data Engineer
A company building an in-house Data Science team should first recruit a Data Engineer. They are expected to build and maintain reliable ETL (Extract, Transform, and Load) systems to collect data from various sources, and store relevant and useful datasets for colleagues to access and utilize. The right candidate to help a company build this would be well-versed with SQL & data architecture, and cost $100K / annum on an average.

2.  Business Intelligence expert
The next step is finding a way to convert the large amounts of collected data into a form which can be easily visualized and understood. Dashboards and data reports are just some of the ways in which data visualizations can be constructed. Business Intelligence experts can parse through data and find ways to visually represent the most relevant data in the most compelling way possible. The right candidate to help a company make effective and useful data visualizations and reports will cost, on average, $80-85K annually.

3.  Machine Learning expert
Putting all the existing data in a visually accessible form is one thing. Building customizable models which can help you predict outcomes for variations across different factors is an incredibly powerful tool. Machine Learning experts can build predictive data-driven models which help optimize business operations more efficiently. A Machine Learning expert, for example can help companies predict logistics costs across a variety of circumstances, or predict customer demand to streamline inventory and optimize your inventory turns. This will cost, on average, $150-$175K per year.

3.  Data Analyst
Even with all the dashboards and predictive models in the world, the questions that data pose to a business and the answers data provide needs interpretation. Data Analysts can review existing data from dashboards as well as insights and possibilities from predictive models, and weigh them against organizational constraints and goals. Through this interpretation, Data Analysts can provide clear and insights that can translate data into action and business value. A Senior Data Analyst can help companies harness the full power of their collected data and will cost, on average, $80K – $100K annually.

Cumulatively, hiring these 4 resources to build a Data Science team will cost companies well over $400K annually.

The cost of data-driven business value
Such a high cost is a heavy cross to bear, but large enterprises happily bear it. For them, it is an investment, the returns of which far outweigh the costs. Merck, the $40B pharmaceutical giant, developed an internal data usage and analysis system which reduced average lead time by 30% and reduced average inventory carrying costs by 50%. For an enterprise of Merck’s size, the cost of implementing such a system is returned multiple times over by the benefits they derive.

Optimized outsourcing
For smaller companies with much shallower pockets than Merck, a full in-house team is not a viable option. However, they can still access the benefits of using Data Science. They can build a hybrid team to ensure that they get the most out of their data at limited expense.

One very cost-effective way to reap the benefits of Data Science for a growth-stage business is to outsource it to specialists. Atidiv Data Science, for example, provides talent augmentation retainers and specialized project-teams to provide your business with a full spectrum of Data Science solutions, ranging from data analytics, visualizations, predictive models, to building credible business insights from data deep dives, at a very reasonable cost.

Have you tried setting up an internal Data Science unit? Do you have any reflections or stories about the experience to share? Feel free to reach out to us at datascienceteam@atidiv.com to tell us more about it.