The 7 Steps for an Analytics-led Digital Transformation

In the current age of AI, all digital transformations must be analytics-led. Learn the 7 steps needed to realize the promise of an analytics-led digital transformation.

Pranay Dave
Pranay Dave
5. Juli 2022 5 min Lesezeit
Analytics-led Digital Transformation

Digital transformation is on the top of the agenda for most CxOs of enterprises. In the current age of artificial intelligence (AI), all digital transformations must be analytics-led. In this post, I will go through the 7 steps needed to realize the promise of an analytics-led digital transformation.


In most organizations, important data remains underutilized, unintegrated, and inaccessible by those who need it most. Businesses have awakened to the fact that data – their data – is an undeniably great asset which contains hidden truths to outpace the competition. Data is often collected in various source systems and in various formats which can present challenges when companies use analytics to extract the value inherent in the data. Structured and unstructured data in mass is hard to massage into usable formats and often, analysts will use samples or sub-sets of the data and hope for the best outcome.

A better approach to facilitate digital transformation is to leverage a flexible data architecture that can process the data at the source as well as integrate it across deployment environments, in the cloud as well as on-premises. A flexible data architecture requires a powerful data fabric that provides intelligent data orchestration and push down processing. It lets you bring all your data together when integration is required, and it lets you leverage data where it resides when the costs of data movement outweigh the benefits of integrated data. An intelligent query fabric helps you manage costs and unlock and leverage all your data.

Organizations can take advantage of data integration across their ecosystem to save costs associated with infrastructure and time to value, leading to enhanced customer experience and improved revenue and market growth.


The key to digital transformation is to understand how data will be able to deliver the breakthrough transformation. This is done by people such as business analysts, who have competence in both data as well as the business. The data needs to be explored at scale to see how it can be useful in solving complex business problems which can lead to digital transformation. With an ever-growing amount of data, the data exploration must be done at scale as well as across various data formats. For example, in order to determine any correlation between product sales and customer product comments, it is required to explore data across sales for millions of products as well as potentially millions of customer comments. You need the capability of hyper-scale data exploration with unlimited joins on massive amounts of data using multi-dimensional scaling, leveraging various data formats, and an agile approach for exploration. Such hyper-scale data exploration will enable you to prioritize business cases which contributes to ROI in the digital transformation.


In the current digital age, AI is becoming ubiquitous and central to digital transformation. Enterprises that will be able to pick up the pace and accelerate AI initiatives will be ones that outshine and outperform the competition. Many companies make mistakes when they think that accelerating AI initiatives means staffing more data scientists and machine learning experts. However, one of the most time-consuming phases for AI is data preparation.

The way to tangibly accelerate AI is to have integrated data as well as perform data preparation directly inside the database. This reduces the movement of data around for analytic purposes. You can also enable reuse via the feature store to drive collaboration of cross-functional teams.

As you accelerate your AI initiatives, the number of AI models which are developed will exponentially increase. With lots of AI models, it is required that they are stored securely and monitored continuously using the ModelOps approach. Such an approach is helpful in governance and life-cycle management of analytic models, as well as enabling reporting on lineage and potential model drift. This will ensure that you will not be overwhelmed with an exponential increase in number of AI models.


There is no free lunch. Breakthrough digital transformation requires solving the hard and complex analytic challenges. Solving simple analytic problems will only lead to incremental value and not a digital transformation. It is nice to have incremental value, but with ever-growing competition, you need to take on the tough cookies to disrupt the market.

Complex and tough analytic challenges are not difficult to find in large enterprises; forecasting millions of products sales, real-time fraud detection on millions of simultaneous web sessions, or IoT robot anomaly detection on billions of sensor readings to name just a few.

To solve the most complex analytic challenges, a robust set of analytics are required such as pattern analysis, machine learning/AI algorithms, space and time analytics, text analytics, and more. In addition to powerful algorithms, it is important to bring analytics to the data and not the other way around. This ensures that there is minimum data movement and that the analytics are executed at scale on complete datasets.

The power of advanced analytics at scale with minimum data movement is the secret to solving the toughest analytic challenges.


The reality of the current analytic landscape is that there is no one tool that will meet all your analytical needs. Fortunately, with the cloud, all required tools are just a few clicks away. You can leverage tools of your choice for various purposes, from open-source to vendor software.

Freedom is good for data science work, and at the same time, it is also important to have an open platform, which will help you integrate work done by open tools on a common database as well as operationalize at scale. This power of open as well as connected analytics is becoming a must-have in the cloud-era.


The worst thing that a digital transformation initiative fears is a Proof-of-Concept graveyard. When you invest a lot in top data scientist talent as well as all different tools, you need to get a return on investment (ROI) to justify the actions taken for the digital transformation. You can do that by giving top priority to the operationalization of AI and advanced analytic work.

Operationalization is much more than AI model scoring. Operationalizing AI requires integrating AI model predictive scores with operational data. For example, if you have a model which predicts manufacturing equipment failure, operationalizing the AI model means also finding out where the equipment is located as well as what manufactured components will be potentially impacted by the predicted failure. This requires storing AI models directly inside the database, thus facilitating seamless integration between AI model scores and operational data.


Digital transformation is not just about advanced AI and data analytics. It should also deliver all benefits at low TCO as well as provide best-in class security to the most valuable asset—your data.

Today, the cloud provides unlimited resources that can be provisioned on a whim. Unfortunately, with abundance comes carelessness and waste. Many of the database vendors today have not had to learn about efficient processing. But not Teradata. We are the gold standard of efficiency and the best price performance ratio.

In addition, it is important to have flexible pricing models and deployment choices without risk of vendor lock-in. This ensures that you can do analytic-led digital transformation with confidence and not fear of cost over-run.

In conclusion, analytic-led digital transformation is what you need in the compete and disrupt in the age of AI. You now have the 7 key factors which will make analytic-led digital transformation a reality.


Über Pranay Dave

Pranay is Director for Product Marketing at Teradata. In this role, he helps customers and prospects understand Teradata's value proposition. Combing strong technical data science and data analytic skills, he participates in technology evangelisation initiatives.

In this global role, he participates in developing market strategy that drives product development delivering transformational value. Earlier he has worked as Principal Data Scientist enabling customers to realize business benefits using advanced analytics and data science. As a recognized expert in Teradata Vantage, Pranay is also a regular speaker at Teradata internal and external events. He is recognized as a top writer for AI in digital media. Pranay has degree in Data Science, MBA and Computer Engineering.

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