For the last few years, data science jobs have been considered as the “hottest” titles in the IT and Cloud industries. The new-age data scientists are still finding their groove in the market, trying to understand what really happens when unsupervised Machine Learning models fail to deliver results as per expectations. The answer can be found with trained data scientists, who by nature of their experience and skillsets, are also the de facto analysts business analysts of the highest quality. As trusted advisors to decision-makers, these data scientists with proven analyst skills more often than not lead the digital transformation journeys for their organization, helping IT and Security, Finance and Employee Management teams to come together and shape the tech culture. Something which you would often find as ‘missing’ in the traditional IT and services companies — so, what do you think really brought about this sudden change in business outlook where every organization, big, small, or a startup, prefers to hire a team of analysts led by skilled data science professionals before they launch their product or services?
The answer is clear.
Customer demands delivery of products and services in an agile model, where the experience of using the product has taken priority over all other traditional product management aspects, including pricing, competition, and value chain. In fact, in most data analytics certification courses, professionals are trained to understand how they can use consumer data to improve experiences across various channels of engagement and interactions.
In a recent virtual seminar that I attended dived extensively into the role of data analytics teams in Agile projects. The presenter and speakers spoke directly to the topic “How to make data work for you in agile work conditions.” I had noted down 3-4 pointers that I think should be shared with you.
Analyst Culture is Key to Product-driven Companies
By nature, analysts are trained to be project-centric in their approach. These scenarios mostly arise in projects where the product is new and not enough A/B testing and quality inspection has been performed. In addition, the lack of historical data on user experience, brand awareness, market intelligence, and other key product marketing related factors also affect the way companies see analysts in their current scheme of things. Due to the massive ingestion of Big Data and the explosion of the mobile user base, things have changed dramatically over the course of the last 5-6 years. Now, every company is a mobile-first organization that prefers to test their products and work on multiple projects simultaneously.
This approach has led data analysts to don multiple hats at the same time, managing more than one project, all targeting the success and consolidation of one or two products, at max.
Project managers work with the trained BI professionals from leading data analytics certification courses to redefine the norms of “on-time, on budget, and precise” delivery approach. Agility, truly, has changed the whole game for product-centric companies in 2020.
Data Analytics is a Company-Wide Application
Companies like Salesforce, Oracle, IBM, SAP, HPE, and other leading Cloud application and software providers have shown how the world of data analytics can be used to improve organizational structuring and productivity. This is achieved by simply adding new technology stacks for the various departments. For example, Oracle Marketing Cloud for automated marketing and advertising campaigns. Then, Salesforce Commerce Cloud for managing a bulk of Sales and contact center. We also have Microsoft and Google redesigning their enterprise collaboration tools for connecting every employee to the hierarchy, with single most objective, “build a data-driven company” with analytics and intelligence as a culture thing.
Data analysts are training senior peers to manage their dashboards and reporting tools to improve departmental productivity at a cost that is 50% less than what it would take for outsourced projects.
Have Data? More Power to You
This easily was my favorite learning from the various virtual seminars. Data Quality Management (DQM), governance and privacy compliance, AI ML, and Virtualization of data centers have become the norms of running a successful analytics company. Having data and securing its framework yields unchallenged results for the company, resulting in big perks for the data analytics team and managers that are leading the effort. Scenarios change; the principles don’t.
Therefore, business analysts are now accepted as the new league of future data scientists who, if trained in AI ML models, can provide more value and better outcomes to any business operation compared to other options.