As information becomes a contributor of organizational strategy, data analysts became critical skillset of an organization knowing the core asset of the company that generates decision making; DATA. New leadership roles such as Chief Data Officer (CDO) evolve creating strategies and governance around data assets building business competencies, maximizing the value of information. As a data architect, I have strategized around Enterprise Information Management (EIM). However, as a data analyst, I find myself contributing all various facets of EIM including data governance, metadata, data quality, master data, and analytics. Here is how I find a data analyst contributing towards EIM:
- A key attribute of data governance is data stewardship. Whether the focus is strategic, tactical, or operational, a data stewardship working group is a key contributor towards the governance council
- The return of investment of data governance is quantified by reducing the time taken by data analysts to analyze and resolve data issues
- Although the data ownership of a subject area is often assigned to a business owner (director level and above), data stewards are often accountable to make decisions and provide directions on the validity and quality of that data from a consumption and provision standpoint
- Data analysts are often responsible to review new projects and provide SME oversight to define data requirements
- Data analysts act as business analysts mastering data and defining rules for specific domains such as customers, employees, products, or territories
- Data analysts are responsible in identifying and defining metadata flows and process flows
- Consistent interactions with business users enable data analysts to define business glossaries that can create a bridge between business and IT
- Data analysts are increasingly responsible to define, test, and validate measures and metrics in both reports and dashboards, which will enable business users to take appropriate decisions
Gartner indicated that by 2017, 33% of the largest global companies will experience an information crisis due to their inability to adequately value, govern and trust their enterprise information. In addition, data quality impacts business driven initiatives by only achieving 40% of the anticipated business value from projects and impacts labor productivity by 20%. Effective data analysts can create and measure business metrics and correlating them to financial outcomes improving operational efficiency, business processes, analytics, and overall productivity for decision making.
A data analyst can be called as data steward, business analyst, technical analyst, database analyst, or systems analyst. However, the core skillset of the data analyst is to realize the value of data and find opportunities and flaws in the data they explore to convert data into information. I get excited when I discover opportunities in the data I am dealing with which I have never thought about. In my experience, the best way to learn a business model is to deal with the underlying data that justifies the business.
In the next of this series, we will discuss some of the industry leading tools assisting data analysts to perform their job at the highest potential.
