Technology Executives: Accelerating data analytics and insights through new thinking in these uncertain times

Technology Executives: Accelerating data analytics and insights through new thinking in these uncertain times

The time has never been more urgent for companies to use their data to make better informed decisions. No matter what industry you are in there are both challenges and opportunities.  How do you identify and prioritize expense reductions? How do you shore-up existing revenue streams and find new ones? And how do you do all this without jeopardizing the long-term viability of the company?  Those companies that master their data & analytics capabilities and the ability to turn insights into actions, will not only survive, but will create sustainable competitive advantages that few competitors will be able to match for a very, very long time.

The speed needed to make decisions has almost never been this quick.  Even the effects of the “market crash” of 2008 was more gradual over a period of months, not days, and the end was clearer.  Most companies rely on a very traditional process of getting to insights – prepare the data, open access to the most experienced people, find insights and recommendations, and convince a committee to make changes in their operations to meet a business objective of increased revenue or improved profits. Unfortunately, this takes time, which most of us don’t have right now.

So, what slows down getting value from your data assets and which self-imposed roadblocks should be re-evaluated?  Corporate policies and procedure are in place for a reason.  Assets and secrets need protecting, employees need to be safe, operations need to be efficient, etc.  Sometimes we forget that policies are also a balancing act between risk and benefits. Those companies that have become too risk adverse, might very well have self-imposed roadblocks that are crippling the company at a time when speed and innovation is most urgently needed.  Take for instance, a company’s work-from-policy. Well, I would imagine that nearly every company that could have remote workers have had to revise those policies – and rapidly put in the infrastructure to support the change in demand. 

Remember, the prize is profitability- and revenue-related business outcomes. Let’s look at a few of these roadblocks that prevent quicker, better decision and what an alternate mindset might look like:

1. Should I leverage the Cloud? We have seen over the past 18 months a monumental shift in the mindset about the Public Cloud providers like AWS and Azure.  The biggest hesitancy was security. More and more companies are now comfortable with the maturity of these massive vendors and the reality that no single company could possibly outspend Amazon or Microsoft to protect Cloud assets. Even large life sciences and financial companies have embraced the Cloud.  I would imagine that most companies are already using a SaaS tool that is hosted in the Cloud. So, why are so many security policies still centered about protecting a boundary that does not extend to the Cloud and does not leverage the Cloud provider’s infrastructure? And why are many companies still insistent on having their own data centers or metal?

Alternatively…. Start small with a locked down Cloud environment that leverages infrastructure (and security) as a service.  There is no quicker method to get new assets spun up. And there is nothing wrong with your data & analytics project in the Cloud to start out by having no access to your source systems.  A bulk import of data is usually sufficient for getting to insights quickly. Once the main goals are accomplished, connections to the source systems and relaxing of restrictions can follow when time is more abundant.

 

2. Is our data valuable?  We have seen a major shift in mindset on whether data is valuable.  I’m not sure how many times I’ve heard about data being the new oil or equivalently valuable resource.  Years ago, our clients would ask us to prove the value of data & analytics which would start a Catch-22 conversation that ended in frustration on both sides.  Now, companies in nearly every industry have seen use cases in their industry or can quickly list the top 10 questions they need an answer to and what it would mean. We are becoming more data driven.  However, we still have numerous prospects that are not able to convince their peers in the C-Suite that their data is valuable.

Alternatively… Pick a single business outcome that a peer would be very interested in.  Something that can be used to showcase the value of your internal data assets while establishing a foundational Cloud data architecture. In today’s business environment, a cost reduction-based business outcome would get everyone’s attention. I would imagine few would push back.

 

3. Are best-of-breed SaaS tools better than all-in-one traditional ones? The Cloud data ecosystem has exploded and has hit critical mass.  We are seeing more and more “born-in-the-Cloud” data & analytics vendors that are truly leveraging the elasticity of storage and compute power in the Cloud.  These new vendors might not have all the features of traditional tools, but they are best-of-breed in their respective space. Traditional vendors have done a “lift-and-shift” of their technology to the Cloud and usually cover several components because integration was so difficult.  Take for example a BI tool that does ETL, Data Warehousing and Visualizations. It is not hard to see that an ecosystem of three different specialized vendors that work seamlessly together increase speed to market, flexibility and reduce vendor lock in. Can one vendor have enough resources or focus than three separate ones?

Alternatively… Now, speed to market is critical so consider only using SaaS tools with low upfront and exit costs.  In the Cloud data ecosystem, there are numerous born-in-the-Cloud tools that will get the job done, quickly. To provide maximum flexibility, go with best-of-breed in their respective area and don’t be concerned with having more tools. At the end of the day, if the tool does not work, replace it with a new one to get you to value from your data assets.

 

4. Does the solution need to be bullet-proofed before users see it?  Many companies have a set process to move new technology into Production.  These processes are critical to reduce errors and disruptions. Data & analytics projects are no exception. Data needs to be prepared, validated and verified before the masses see it.  This approach, however, takes time, and a lot of it.

Alternatively…Consider getting the raw data in all its glory to a focused group of users in the business that will be making the decisions.  With the proper expectations, this approach not only allows the business users to see progress, but gets them involved sooner to help improve the quality and usefulness of the data.  Making decisions on good but not perfect data is better than decisions on no data at all. It is perfectly fine to allow these users access to data in a Development environment. The Productionalization of the data ingestion, preparing and storage can come later once value has been proved.

 

5. Are my internal resources up to the challenge? Companies usually like to try to do things themselves first.  In the Cloud data & analytics space, we have seen numerous companies tap their internal data professionals to learn the Cloud data ecosystem, do a proof of concept or two, and then figure out which tools might fit with their existing technology stack.  This process could take months between training, tool selection, and learnings from mistakes.

Alternatively…. Most companies want to bring their people along with new technology – that can be done later when there is more time. Companies also think internal resources are less expensive – but what is the opportunity cost of not acting now. Having a trusted partner that already has the experience will greatly accelerate any Cloud data & analytics project.  

 

To wrap up the conversation, let’s talk about what this might look like in practice.  What if you were able to abbreviate the vendor selection process and just select born-in-the-Cloud SaaS-based Cloud data & analytics tools that specialize in their respective space?  As an example, this might be a combination of Snowflake (for Data Warehousing), Sigma (for Excel-like business intelligence), and Matillion (for data ingesting and transformations).  Next, what if you engaged with the business users and reset expectations?  You could quickly identify only the data you needed to keep the effort focused and let them know the data is not perfect and they were part of the verification process.  With the data identified, what if you then just ingested bulk data from your source systems into Snowflake without a connection to source systems? Access to even week-old data is more helpful than no access at all.  Once in Snowflake you can prepare the data with standard SQL or leverage Matillion. And lastly, with your “good-enough” data, connect to Sigma as a Business Intelligence tool we affectionately refer to as Excel of steroids.  With the accelerated access, decisions can then be made immediately and put into action. Once everything stabilizes, then it’s time to Productionalize your efforts and expand your data & analytics capabilities. 

With a different mindset, actionable insights that will affect the top- and bottom-lines could be possible in a matter of weeks, and not the traditional months it usually takes. What would this level of speed to insights do for your company and its outlook?

 

About Integress

Integress, the Philadelphia-based Data Analytics Company, helps companies understand, build and accelerate their Cloud Data Ecosystem, Machine Learning and AI capabilities.  

Through a combination of business consulting to the C-Suite and deep data expertise, we deliver measurable value from our client’s data assets that meet business outcomes of increasing revenue and profit, creating new revenue streams, gaining competitive advantages, and becoming customer-centric. 

Since 1998, we have been a trusted voice working alongside CxOs to meet corporate goals that leverage data as a strategic asset, while accelerating time to market, reducing execution risk and ensuring business-to-IT alignment. 

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