The Most Effective Enterprise Data Analytics Strategies Always Look Beyond Technology
With AI bringing on a new phase in data analytics, we examine historical trends and the best practices enterprise technology leaders should pursue.
- By Anil Inamdar
- October 4, 2023
Data analytics strategy is again at the forefront of technology conversations -- this time driven by the emergence of generative AI. Enterprises recognize its vast potential to implement transformative new capabilities and competitive differentiation. However, as lessons from past cycles and evolutions in data analytics indicate, it won’t be technology alone but rather surefooted leadership and a willingness to overcome legacy thinking that will enable some enterprises to rise above others on the generative-AI-enabled competitive landscape.
With AI making waves that will soon lead to a sea change in the influence of data analytics, let’s examine what historical trends have to tell us about this current cycle and the best practices that enterprise technology leaders should pursue.
Welcome to the Data 4.0 Era
The rise of AI/ML ushers in a distinct fourth phase in data analytics modernization, building on a series of evolutions dating back to, well, the dawn of computing. The first phase -- spanning from the era before personal computers up to the mid-80s -- saw analytics take a back seat while organizations learned to automate manual, non-computerized practices. Modern data analytics processes then arrived with Data 2.0, as enterprise data warehouses (EDWs), business intelligence (BI), master data management (MDM), and other practices turned data analytics capabilities into a competitive advantage.
The mid-2000s saw the rise of the Data 3.0 era that defined practices still familiar today, with data serving as a disrupter powering new business models and competitive processes for Amazon, Uber, Airbnb, and others while digital transformation became priority one for many organizations. The now-emerging Data 4.0 era takes the next step: incorporating AI/ML and new data science techniques into data analytics frameworks.
To paint with a relatively broad brush, an enterprise’s age tells us a lot about the strategic hand it has to play. Digital companies born today are starting with Data 4.0 processes. Many legacy companies are still in the Data 3.0 era and must focus on digital transformations to successfully adopt current competitive capabilities. The rare commercial companies and government bureaucracies still in the Data 2.0 phase have far more strategic transformation ahead of them.
My experience in the field (which now spans nearly 30 years) tells me that most legacy companies have a strategic data analytics vision. However, the struggle lies in getting executive leadership to act on it. The right leadership is essential to shifting a company culture away from legacy thinking and adopting new operating models alongside modernized data analytics tools and technologies.
Putting Data-Driven Decisions Into Action
Data analytics now delivers decisive competitive advantages: for example, the phenomenal success of Amazon’s and Netflix’s recommendation engines is totally driven by customer data. The key to enterprise accomplishment here is bridging gaps between data insights, decision-making, and action. Ideally, insights should directly impact business operations, continuously and on the fly. Turning insights into the best decisions is a matter of tools and technique. That said, the jump from decision-making to action is the most challenging because effective execution and truly embracing new approaches requires clear leadership.
Pressure from competitors and shareholders and the demonstrable need for more customer-centric approaches are driving enterprise data analytics strategies forward. Business and technology leaders are realizing that a coherent data strategy is required to remain competitive and relevant. However, legacy mindsets, lack of executive focus, and outdated tools and technologies can present difficult obstacles. The emergence of AI/ML and generative AI in data analytics is only deepening the impact of the leadership deficit within legacy-minded organizations. Although digitally born and digitally transformed companies are embracing AI/ML to update their data strategies and reap profound dividends, legacy organizations are still struggling to grasp the significance of AI/ML -- and quickly falling further behind.
Democratizing Data and Eliminating Silos
For decades, IT has been the custodian controlling all data access. However, data-savvy organizations now realize that empowering the right internal personnel to access the right data at the right time enables quicker decisions and competitive advantages. Advanced tools and technologies -- cloud storage, data replication, data virtualization, and self-service analytics applications -- are making it easier to provide safe and secure data access to more people.
AI/ML and generative AI, where analysts can simply ask questions in plain language and receive immediate answers based on vast troves of enterprise data, are further democratizing data and freeing it from silos. Fast forward a few decades, and I believe AI/ML will automate all stages of the data pipeline, from data capture to ingestion, storage, processing, analytics, and insights. AI/ML will eliminate differences among unstructured, structured, batch, or streaming data sets and automate security and governance, fulfilling the data democratization dream.
Strategic Data Analytics Best Practices
Enterprise technology leaders should pursue these best practices as they grow and modernize their data analytics capabilities:
- Foster data-driven decision-making
- Democratize data access and eliminate data silos
- Introduce customer-centric cultural practices and goals
- Focus on results instead of processes
- Take calculated risks and avoid becoming risk-averse
- Don’t be shy about embracing cutting-edge data analytics technologies, such as AI/ML; they may very well be required to stay competitive
At the same time, recognize that a poorly managed strategy can be worse than the status quo. Understand that advanced tools and ambitious strategies are only beneficial when properly implemented, executed, and continuously enhanced.
Leadership and Culture are the Keys that Unlock Technology’s Potential
I’d argue that the toughest challenges to effective data analytics aren’t related to tools or technology or even data itself, but rather people and culture. No matter what cutting-edge tools an enterprise uses, if they are locked into legacy thinking -- lacking data-driven leadership and focused on process rather than results -- they will not be able to use data effectively.
Technology, culture, and best-practice adoption flow down from executive leadership. If the right executive and cultural support are in place to embrace data and digital transformation, tools and technologies such as AI/ML and generative AI can deliver their full benefits, and competitive advantages will inevitably follow.
Anil Inamdar is the VP and head of data solutions at Instaclustr by NetApp, which provides a managed platform around open source data technologies. Anil has 20+ years of experience in data and analytics roles. He regularly speaks on Cassandra topics and best practices. Prior to Instaclustr, he held data and analytics leadership roles at Dell EMC, Accenture, and Visa, among others.