Another blog about digital transformation? Not quite. I’m not going rehash the 2014 thought leadership about how disruptive trends such as mobile, social, and IoT are creating new threats and opportunities. And how business that become “digital”, or data-driven, can capitalize on the changing behaviors and expectations of today’s consumers.
Nope. This blog will examine why the business world has been talking about digital transformation (DX) for more than five years but few organizations have made significant progress. And, it will outline what your business do this year to accelerate it.
The unofficial definition of “insanity” – doing the same thing over and over and expecting a different result –offers a clue.
Instead of talking endlessly about the concept of DX or its benefits, it’s more productive to examine the obstacles in the way so you can make real progress – this year.
I started hearing about digital transformation circa 2012. I disliked the term: how big of a topic it was, how there was no such thing as a DX product yet every tech vendor was trying to sell it, how nebulous and unintuitive it was. After all, nearly every technology used by a business is already digital, so what else needs to be transformed? Well, I’ve slowly realized that DX is more about changing mindset and processes than it is about new technology.
The challenge of DX is that it covers so much ground that organizations don’t know where to start, who to assign it to, and how to measure its impact. The idea of DX is that we’re supposed to apply “digital” technology to become “data driven” which means connecting all of the information about every aspect of the business, and removing human guesswork, estimations, and opinions from business decisions and processes.
Consumer technology evolves quickly, and so do individual consumers – changing continuously behaviors and expectations. But businesses, on the other hand, tend to stink at adapting. By most measures, it would be faster and easier to create a data-driven business from scratch than it would be to adapt an existing business to become data driven. This gets to the idea of Digital Darwinism, as outlined in many articles by author Brian Solis, as well as Tom Goodwin, which Brian Solis refers to as “when technology and society evolve faster than your ability to adapt.” The systems, processes, tools, and mindsets of enterprises are much slower to adapt than their customers. So how do we change that? I’ve got some ideas.
Let’s examine some of the biggest DX obstacles that business face and what they can do to make progress this year.
- Obstacle 1: We’re collecting the data, but we can’t access it. One of the most common problems enterprises face with respect to leveraging their data is access. Company data now lives in many places which creates silos that complicate efforts to perform analysis across disparate data sets. Additionally, the data itself now spans many formats, including real-time streaming data, unstructured data, social feeds, log data and more. Many organizations have attempted to resolve this with enterprise-grade, on-prem data lakes, which can ingest store and manage vast amounts of structured, unstructured, and real-time data from internal and external sources so that the data can be made available to IT teams and data scientists for analytics. However, these deployments are complex and time-consuming, requiring expensive infrastructure, extensive integrations of hardware and software, and deep technical knowledge in house. For these reasons, many data lake and DX initiatives are stalled.
- Resolution 1: Low-code cloud data lake. To address the need to gain unified access to all types of data while avoiding the costs, complexity, and skill sets required for enterprise data lakes, business and business-focused IT teams should consider a pre-integrated, low-code data lake that can be deployed directly to the cloud, such as our TCS Connected Intelligence Data Lake for Business. It is available for a 30-day free trial in AWS Marketplace. The approach addresses all of top obstacles to data lake adoption. It is cost effective. It can be deployed quickly. It eliminates the dependency on IT, and it does not require deep technical knowledge to operate. Business and IT Teams can quickly on-board, manage and govern data for analytics with a simplified, self-serve Hadoop data lake platform. It has an easy drag and drop interface to model, catalog and automate data ingestion with no additional coding. It also serves as a single data platform to develop multiple analytics use cases securely with fully featured administration, workflow automation, security policy and user access control.
- Obstacle 2: We have the technology, but we don’t know what to do with it. Another top challenge that business teams face is that although they have data, Hadoop, and some analytics and visualization tools, they haven’t identified the right use cases for data and analytics and they don’t have the skill sets to develop the data and analytic models to surface the insights they need to drive their business initiatives. Because of this, many data analytics initiatives are nothing more than science experiments with no path toward monetization.
- Resolution 2: Pre-built, industry specific analytics use cases integrated on an extensible analytics platform. Forget about spending the next year or more architecting and implementing a fully featured data and analytics platform and then hiring teams of data scientists to help identify, prioritize, build, and operationalize analytics use cases. Instead, you can get a pre-integrated data and analytics platform (including the data lake, analytics, visualization, administration, and governance) that is built on open-source software and leverages your existing IT investments. And, you can get pre-built, industry specific use cases that eliminate the need for hard-to-find data scientists while speeding the time to value. For example, our Customer Intelligence & Insights software has versions for Retail, Banking, and Communications to improve targeting, personalize the customer experiences, and recommend next best offers and actions. And, the same underlying Connected Intelligence Platform also supports a smart cities version for optimizing energy, water, and transportation within municipalities. This approach puts the power of data science in the hands of business users quickly, easily and cost effectively.
- Obstacle 3: We have the insights, but they aren’t actionable. The last major obstacle to many digital transformation initiatives is surfacing insight-driven recommendations and delivering them to key stakeholders and front-line customer-facing systems. Some organizations have made progress at finding the hidden insights in their data that are relevant to their top-level strategic business initiatives, but they don’t have a way to convert them into recommendations for actions and then deliver them in the right way, at the right time, to the right business entity. The problem here is around data science, artificial intelligence (AI), machine learning (ML), app-dev, and integration.
- Resolution 3: Bundled AI, ML recommendations delivered via APIs. Rather than taking a rip-and-replace approach to EDWs, CRMs, marketing automation platforms, and customer data platforms, you should seek to compliment and augment your existing systems with a flexible analytics platform that easily integrates with those adjacent systems and apps via APIs. In other words, don’t seek to replace your marketing automation system. Instead channel the data-driven recommendations to it and let it continue doing its job of automation. This minimizes disruption to functioning systems while eliminating the human guesswork from telling these systems what to do next to optimize a campaign or customer interaction. Our Connected Intelligence Platform and Customer Insights & Intelligence software leverage this approach to simplify and speed the delivery of timely recommendations while reducing TCO.
No matter where you are in your digital transformation journey, addressing these obstacles can help your organization put points on the board. And that’s key, because overcoming inertia is often the biggest obstacle of all.