Data-Driven Digital Transformation Means Cloud Data and Analytics

Last year, Gartner declared that on-premises databases are now legacy technology. Are we ready to say the same for analytics and BI?

Why Now?

Organizations have recognized the cloud opportunity for years now. Most, however, lacked a cohesive strategy to get there. There was little urgency to make this shift. Organizations could get by using their traditional on-premises infrastructure to meet their data and manage their business. 

In the last few years, everything has changed. Extreme economic conditions and the pace of change have forced organizations to adopt a “cloud now” strategy for data analytics

Here’s what’s driving this change.

Digital Transformation 

Looking to capitalize on new opportunities in a digital world, organizations were well on their way to data-driven digital transformation. With more data now originating in the cloud, many enterprises were implementing their data modernization plans before the pandemic hit. The pandemic, however, has further accelerated these plans. A Gartner survey of boards of directors shows 69% of organizations have accelerated their digital business plans. 

Speed to Market and Agility 

The global pandemic has pummeled certain industries, but the ones that had already begun their digital transformation and cloud journeys have proven to be more resilient. Instead of focusing on upgrading and maintaining on-premises applications and infrastructure, these organizations have moved more quickly, and they’ve been able to nimbly focus on rapidly changing processes. Organizations with more resilient infrastructures and better data (internal and external) have pivoted faster and will continue to benefit from the first-mover advantage. 

Elastic Compute 

Cloud computing enables new forms of analytics with greater amounts of data than on-premises deployments provide. On-premises analytics can prohibit experimentation because the data infrastructure has to be designed for peak demand—even for a one-time analysis. During a debate about the trade-offs of cloud, the CDO of a large financial services firm shared that the analytics his company is performing today was simply not possible before. They simply didn’t have the budget or “people power” to scale the on-premises data center. Cloud enables an organization to add compute power, experiment, analyze, and then remove what is no longer needed or return to steady-state demand. This can be done in hours and days as opposed to potentially months and years for on-premises deployments. 

IoT and 5G: Analytics Accelerants 

First-generation on-premises data warehouses focused largely on internal, transactional data. With the rise of the Internet of Things (IoT), data is being generated from a range of devices including cars, household appliances, cell phones, medical devices, smart meters, and more. Global telecommunication companies are in the process of rolling out 5G, which will generate even more data, faster, touching every sector from healthcare to entertainment to manufacturing and smart cities. According to research from Seagate and IDC, the volume of enterprise data will grow 42% annually from 2020 to 2022. The ability to store and analyze this data will be a critical competitive differentiator for rolling out new use cases, improving customer experience, and driving revenue. In this way, cloud, IoT, and 5G are the perfect storm for analytics. The leaders are aggressively moving to the cloud ahead of these trends, while laggards, who remain largely with on-premises deployments, will face a sharp demise. 

A Mature Market 

The cloud analytics market has matured, now rivaling and surpassing the levels of security, administration, and control once found only in mature on-premises deployments. And yet, not all have evolved at the same pace, forcing organizations to reassess the mindset of buying from big vendor incumbents and to forge new partnerships. 

How analytics and BI has changed from 2008 to 2020.

Follow the Data Gravity

Technical debt is one of the biggest challenges organizations face when trying to innovate. There is an incredible amount of inertia and investment in the status quo. Data and analytics have long followed the data gravity of on-premises business applications. 

That was before Salesforce. And Workday. And Concur. And ServiceNow.

The list goes on and on. According to IDG’s Cloud Computing 2020 survey, 44% of organizations have already moved their ERP and CRM applications to the cloud, with another 31% planning to do so in the next two years. In addition, more data now originates externally in edge data stores, cloud repositories, and third-party data stores. Seagate and IDC estimates only 30% of enterprise data is stored in internal data centers. It’s time for your data and analytics to move to the cloud. 

Don’t let your existing legacy on-premises data and analytics act as a ball and chain that holds you back.

Turbulence Getting to the Cloud

It was only two years ago, when I was a research VP at Gartner looking at data and analytics maturity assessments, when the majority of organizations had no cloud strategy. There was intent to get to cloud, but the approach was much more reactionary than purposeful: Build a business case for one application, usually driven by a line of business. Shout louder. Fast forward two years and the conversation has changed with mandates to digital and data transformation leaders to accelerate cloud plans. 

And yet, the migration is not easy. You do not get to throw away what was built up over decades. As Sully McConnell, the CDO from Hartford, described on The Data Chief Podcast, “One application is so customized, it is cost prohibitive to move it to the cloud. And yet, it's hard, but we're pushing hard, and we're going to stay the course, and we're going to have our entire data ecosystem be in the cloud at some point.”

People change management and reskilling is also a challenge. The way organizations are designed for an on-premises data warehouses is different from the way they are designed for cloud data lakes, data warehouses, and cloud data platforms

As Jon Osborn, a CDO in healthcare, described, “Data still has issues and new tools are needed like Snowflake and ThoughtSpot to actually solve some of these problems where, maybe traditional relational tools just hold companies back. But yet they're so grounded in them. It's difficult to migrate. It's difficult to get any sort of agility when you have tools that have been around for 25 years, but also people with mindsets that are 25 years old.”

With unlimited compute capabilities, customers also have to think differently about who can launch an expensive query, how they are consuming analytics, and when to turn off certain services. As one expert quipped, “worship at the altar of ‘turn that s*** off.’”

Security remains a concern, but one that is largely misplaced. Your data is only as secure as the weakest link, and while last year’s Capital One/Amazon data breach gave cloud a bruising, most of the data breaches have been of on-premises data stores and attributed to poor processes. Cloud does not absolve customers of the responsibility to set privacy and control policies. The difference is more in the levels of certification data and analytics providers have, as well as who has control of encryption keys.  

How to Get There

Making the shift is not easy, but the value cannot be underestimated. Here’s how you can make the transition:

  1. Start small. Start with a high-value use case and with clean, nonsensitive data. Use the free trial of Snowflake and ThoughtSpot to understand the benefits of a cloud deployment and the benefits of modern data and analytics.

  2. Reskill, upskill, and redesign. Proactively upskill data warehouse managers, DBAs, and architects. Revisit security policies and practices for cloud deployment.

  3. Identify when to leave on-premises versus when to shift. Evaluate the highest value uses cases, which often means new data not previously accessible in an on-premises deployment. For legacy data stores, evaluate what to migrate and what can be shifted versus what requires redesign.

  4. Move away from legacy technology. Outsource maintenance for legacy on-premises technology and ultimately shut it off.

At ThoughtSpot, we’re committed to helping you make the transition to cloud data and analytics as seamless and valuable as possible. Learn more about how our new SaaS analytics offering, ThoughtSpot cloud business intelligence, makes that easier than ever.