The Curious Case of building a “Data Analytics” Strategy
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Monica Rogati wrote about the “The AI Hierarchy of Needs” article on Medium 45 months back and shared this insightful pyramid asking “how do you tell companies they are not ready for AI without sounding (or being) elitist – a self-appointed gatekeeper?
Truth being said, a lot of companies still do not realize that it is “data” which is at the core of systems such as Data Sciences and Artificial Intelligence. Many organizations continue to fail when they attempt a major analytics initiative. The reason is that the focus tends to be on the top 3 layers of the pyramid above and not on the rest, which lay the foundation to building a strong “data analytics” strategy.
While a lot of organizations are paying attention to building data models, they tend to ignore things such as data quality and data integrity. “Data will redefine how we think about models,” says Fei-Fei Li, Chief Scientist at Google Cloud, and a Professor at Stanford.
Organizations need to ensure that they have good data management processes. “The best algorithm wouldn’t work well if the data it learned from didn’t reflect the real world.” A better dataset is the first requirement before starting off full steam on Data Visualization, Data Sciences, Machine Learning, or Artificial Intelligence.
While most businesses have a desire for data-driven insight, many are not realizing that ambition. The result is that data management is often fragmented and driven by multiple stakeholders. This leaves organizations dealing with a high degree of inaccurate and disparate data and there are several challenges to maintaining it.
So, what should organizations take into consideration when building a data analytics roadmap or strategy?
Data Volume
We all know that “Data Analytics” requires “good data” but even before that, we need volume. Lot of organizations do not seem to take the volume problem seriously. They somehow think or assume it is available. As Zuci’s Chief Technology Architect Jana says, “Building a data bank is an enterprise problem today and I think very few organizations take it seriously, even though digital transformation is taken with absolute seriousness.”
Organizations need the right people who are provided with time at the start of projects to create robust and effective systems for collecting, curating, and storing data. That is the data team’s most important business objective, not just building a data warehouse and dumping everything there.
Data Quality
The follow-up issue to data volume is data quality. Poor data quality leads to the inability to develop accurate insights/models. It also drives down the productivity of the data team. Data workers spend most of their time trying to piece together existing data to clean it and create better quality. Data scientists report that they spend more than 82% of their work cleaning and preparing data for AI/ML applications.
Organizations need to focus on creating good data pipelines, reliable data flow infrastructure to build good data leading to better accuracy in deriving insights.
Data Governance, Security and Compliance
Taking the data volume, data quality aspects into consideration which are more technology-oriented, organizations should focus on other critical aspects such as Data Governance, Security, and Compliance, which are part of building a good data strategy.
Building a Data analytics roadmap requires the right blend of people, data, products, technology, process, and governance to be successful and productive. In addition to that, an analytics roadmap should start with a comprehensive review of the organization’s business goals and the key metrics they intend to derive for their business.
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