New AI and Data Analytics Trends Highlight Urgency for New Strategies

New AI and Data Analytics Trends Highlight Urgency for New Strategies

A Data & Analytics Strategy is focused on realizing value from your strategic data assets through Business Intelligence, Advanced Analytics/ML/AI and Data Sharing. Having a Data & Analytics Strategy in place has never been more urgent and necessary.

For organizations who have taken a “wait and see approach,” recent trends in adoption (and more directly, investment) are clear signs that the future is now. Are you still sitting on the sidelines? Not convinced of the silver linings in the clouds? Let’s walk through some Data & Analytics Strategy essentials.

What is a Data & Analytics Strategy?

A Data & Analytics Strategy includes not only the above mentioned methods to make insights actionable but the Data Management Solutions that are needed to support them. A Data & Analytics Strategy differs from an Enterprise Data Strategy in several significant ways:

Money Talks

Both strategies are critical to organizations, however the former is traditionally a footnote of the later. We are now seeing organizations creating a clear demarcation between their Data & Analytics and Enterprise Data Strategies. The advantage is significant considering that the global AI and analytics space is projected to go from $3B two years ago to over $90B in 2025 – an indication that competitors are and will be achieving significant returns on their strategic data portfolios.

Aligning Technology With Business Goals

Like any good strategy, your Data & Analytics Strategy should first focus on the business outcomes related to corporate goals that can be met with your strategic data assets. If an initiative does not align with corporate goals, it probably doesn’t belong in the plan…at least not yet. This will require both a business consulting mindset combined with a data technologist’s expertise. Once the business initiatives are identified (the “business consulting” part), next comes the data initiatives, their dependencies and the technology that supports them (the “data technologists” part).

Years ago the data space was relativity boring (OK, I heard that snicker). Projects meandered and expanded, resulting in mission fatigue and severely bloated budgets. Yes, most people working on these projects “knew” they were necessary, but it was difficult to find a clear link to value, let alone to something that people actually cared about or understood.

Today, we are seeing a much different story. Amazon, Microsoft, and Google are investing tens of billions of dollars to get your organization to the Cloud. With all of this investment, the sheer number of new data-related technologies, techniques, and platforms has exploded. The result is that organizations now have access to new capabilities that are enabling a more direct alignment to corporate goals and value realization. 

Faster. Smarter. Better.

In this new reality, organizations across nearly every industry are able to start truly strategic Data & Analytics projects more quickly, with less risk and most importantly with less investment. With this new model, organizations can now invest in multiple projects with the understanding that if some fail, others in the strategic data asset portfolio will more than make up the dogs. For a well-managed analytics portfolio, returns of 3x, 5x or even 10x would not be unreasonable.

Stepping Into The Cloud

To begin to execute on your Data & Analytics Strategy, your team will most likely find themselves in the uncomfortable territory – the Cloud. They are not alone. It has only been over the last 3+ years ago that Cloud adoption has exploded and become mainstream in nearly every size organization from start-ups to Fortune 100 companies.

As a primer, your Data Management Solutions for Analytics (DMSA as coined recently by Gartner to accommodate this new breed of technology and purpose) will include a smaller number of components as compared to your Enterprise Data Architecture:

  • Data Storage (Data Lake, aka raw storage, and Data Warehouse(s) for analytics)
  • Data Loading (moving data from transactional systems into your Data Lake/Data Warehouse)
  • Data Transformation and Orchestration (application of business rules, standardization and data “refinement”).

Luckily, once you have picked your Cloud provider, say AWS or Azure, there are only two main choices for your foundational Analytics Data Store – a modern Massively Parallel Processing (MPP) Data Warehouse – Redshift or Snowflake in AWS and Azure DW or Snowflake in Azure.

An Explosion Of Choice

Unfortunately, from there it gets a little more difficult as each of the Data Management components, BI, Data Sharing and Advanced Analytics, have numerous technology options, depending on your organization’s specific needs. Like any good evolution, the Data & Analytics Strategy builds up the pieces of your architecture while delivering new capabilities and value to the business.

The Data & Analytics space has finally matured to the point where organizations no longer need to on the “bleeding edge”. Instead, smart organizations can now leverage leading edge data technology – and be rewarded with improved performance/profit, increased revenue, more valuable customers, and a competitive, potentially sustainable advantage.


But where do you begin with all of the data technology options out there? Typically our clients will choose one, yes one, specific business use case to pilot (aka, not a PoC). That use case not only proves out the Data & Analytics Architecture but provides a quick-win to the business and gets them engaged and excited. In fact, we are now recommending starting with one of the three “value-delivery” methods – Business Intelligence, Advanced Analytics or Data Sharing.

Most organizations choose to travel this path with an experienced guide. At Integress we are specialists in accelerating data analytics in the cloud. To discuss your Data & Analytics Strategy, including our Business Intelligence and Analytics Jumpstart program, click here or call 610.664.1711.