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Vishal Gupta

Spotlight on Salesforce’s Acquisition of Tableau | Blog

By | Blog, Mergers & Acquisitions

On June 10, 2019, Salesforce announced an agreement to acquire Tableau, a leading interactive data visualization company, for US$15.7 billion in an all-stock deal. Here’s our take on it.

Strategic Intent behind the Deal

The announcement is a masterful move to aid Salesforce’s hyper growth agenda to become a US$28 billion company in three years’ time. In the past 15 months, Salesforce has accelerated the data pivot through its acquisitions of Mulesoft in March 2018 and now Tableau, for a combined value of $22.2 billion.

Given its ambitious topline growth goals, Salesforce has hedged its bet against a pure cloud play. Tableau, which is not a cloud company, runs most of its products on-premise, with over one-third deployments in the cloud. However, last year, Tableau announced that its products will also be available on hyperscalers’ cloud platforms (AWS, Microsoft Azure, and GCP.) Addressing the ubiquity of data in a modern enterprise and recognizing the transition in software consumption pattern, Salesforce is taking an “anytime, anywhere” analytics approach to cater to enterprise’s hybrid cloud-first mandate.

In addition, Tableau’s strong performance against rivals including IBM Cognos, MicroStrategy, Oracle BI, and QlikView makes a strong case for the acquisition, given Salesforce’s big bet on its Customer 360 initiative and its broader foray into empowering clients with data analytics and visualization capabilities.

Enhancing the Data Analytics and Experience Pivot

Salesforce, a veteran in the CRM space, is repositioning itself as a digital experience (DX) platform, wherein it intends to become a one-stop, end-to-end solution for enterprises’ DX needs. It has been making strategic acquisitions over the years to plug in the gaps in its DX platform portfolio to achieve this goal.

SFDC Acquisition blog DX image

Because Tableau and Salesforce’s in-house analytics tool, Einstein Analytics, can easily interoperate, the company will be able to sell a well-packaged data analytics offering. Tableau’s niche capabilities in data analytics will not only deliver an improved data management solution but will also help enterprises form data-intensive strategies and optimize the overall stakeholder experience. And, the acquisition gives Salesforce new up- and cross-sell opportunities, as enterprises will be able to purchase CRM and business intelligence (BI) capabilities from a single vendor.

Gaining a Full View of Enterprise Data

Looking at the timeline of Salesforce’s acquisitions, we see a strategic shift from targeting digital marketing and commerce space toward enhancing enterprise data lifecycle management. Since 2018, Salesforce’s top deals have been to expand its coverage in the data and analytics space. Undoubtedly, the move has given Salesforce a shot in the arm when it comes to showcasing its capabilities across the data management value chain. Tableau sits atop of its acquisitions, plugging in multiple outside data sources and offering an easy to use UI for data visualization.

SFDC Acquisition blog CRM image

Indeed, Salesforce’s acquisition of Tableau is a strategic next step after its 2018 acquisition of MuleSoft. While Salesforce leveraged Mulesoft to create a “Salesforce Integration Cloud” that allows different cloud applications to connect via APIs, Tableau can help it gain deeper insights in this data, in turn driving enterprises toward data-driven decision making.

Data Orchestration Meets Cognitive

We give a thumbs up to this deal, particularly for what it means to the market going forward. Why?

The move fits well with Salesforce’s agenda to move into machine learning-driven analytics. Essentially, it will now have a strong BI tool, underpinned by AI, that will democratize enterprise access to next-generation data modeling and analytics capabilities.  A Tableau-integrated Salesforce Einstein Analytics offering should be able to deliver an intelligent, intuitive analytics and data visualization platform that leverages enterprise-wide data to help enterprise customers, employees, and partners with well-curated insights.

How to Construct a Digital Transformation Analytics Roadmap | Blog

By | Blog, Digital Transformation

Is data really the new oil fueling digital transformation? Absolutely. A company’s ability to make fast-paced, meaningful decisions in a volatile business environment is key to competitive differentiation. Indeed, industry leading enterprises are using data and analytics to adapt to dynamic market conditions, drive continuous innovation, and accelerate the speed of doing business.

However, many organizations are struggling in their efforts to harness the value of data to aid their transformation efforts. The single most important reason for these failures is their technology-first thought process. They invest in the latest big data and analytics tools, AI and ML algorithms, and visualization technologies, and subsequently determine how to drive adoption.

This approach is flawed. Why?

Technology in and of itself does not provide answers to how businesses must adapt for success in a data-driven future. It’s not enough to have the best tools; organizations need to start with a broader vision built on a foundation of business requirements. Companies that succeed at meeting their analytics objectives let business goals drive the technology, and not the other way around.

The business objectives

To develop an effective and value-generating analytics roadmap, enterprises need to start with their strategic business objectives. These tend to fall into three broad categories:

• Top-line growth – Value derived from better understanding potential target segments to enable greater revenue generation. For example, improved customer satisfaction, creating long-term customer loyalty, etc.
• Cost reduction – Value created by leveraging analytics to identify the cost leaks, such as redundancies and inefficient processes, and trim expenses. For example, minimizing procurement spend, plugging revenue leakage by reducing inventory cost, etc.
• Risk and compliance management – Value gained from monitoring, preparing, and managing risk and compliance on a real-time basis, and anticipating any potential risk-related issues, e.g., fraud detection and monitoring.

 

analytics roadmap

The building blocks

After clearly establishing their business objectives, organizations need to make important decisions about four distinct building blocks:

• Data – At the heart of every analytics solution lies data in its raw form. Enterprises need to have a data strategy in place to cope with increasingly large and complex data volumes coming from diverse sources in a wide variety of formats (text, images, audio, video, etc.)
• Technology tools – Core technology tools and platforms for data ingestion, processing, preparation, and visualization are critical. But they cannot be one-off implementations. Enterprises should focus on building integrated technology ecosystems to address immediate, distinct use cases without considering the mid-to long-term creation of sustainable capabilities
• Talent – This requires the creation of competencies around the specific, expected data and analytics capabilities. Given the huge demand/supply gap for data and analytics professionals, particularly data scientists, e-enterprises must proactively and enticingly attract and retain the right talent
• Infrastructure – The focus here is on ensuring that the IT infrastructure can handle the volume, variety, and velocity of the data and the complexity of the analytics.

Once they’ve laid the business objectives and building blocks groundwork, enterprises can develop their digital transformation analytics roadmap. In order to achieve the desired business outcomes from the analytics process, they need to embrace a structured, five-step iterative approach.

Getting this right is critical, and the stakes are high. The organizations that proactively embark on a data-driven digital transformation journey – i.e., every company– will gain a significant competitive advantage. Those that fall behind risk irrelevance.

For more information and insights on how to create a digital transformation analytics roadmap for your business, or to share what you’ve been able to achieve with your roadmap, please contact me at [email protected]