Baking the Perfect Data Soufflé

soufflé

In an Inc. Magazine article, it’s been noted that the difference between complicated and complex is that complicated has components that can be “separated and dealt with in a systematic and logical way that relies on a set of static rules or algorithms.” If we translate this into the world of food, a soufflé can be considered complicated, but when broken down properly, we can produce attractiveness to the eye and deliciousness to the taste.

Data analytics can mean different things to different people. While there is not yet full agreement upon common structure or definition, GAP compares data to a soufflé: if you break its components down into ingredients, we can get our arms around the outcomes, which can produce meaningful results.

GAP thinks about data analytics in terms of:

  • What questions would you like to answer regarding your data?
  • Who needs to be involved to help us answer these specific questions?
  • What resources do we need and what investments are required to make this happen?

We would like to help you clarify the possibilities of data analytics and provide three ingredients to baking the perfect data soufflé.

Ingredient #1: Know the Specific Business Objectives

Clearly defining and quantifying the business objectives, and placing appropriate parameters around those objectives, is the first key ingredient to unlocking the potential of data analytics. Does your company want to:

  • Improve profitability?
  • Automate business processes and eliminate waste?
  • Make better investment decisions?
  • Retain profitable customers and possibly fire unprofitable ones?
  • Target specific clients?
  • Fight fraud and mitigate risks?

Once you identify and quantify the business objectives, you’ll need to determine how a complicated range of data can be aggregated and manipulated to deliver exactly the insights needed for your desired outcomes. You will need to assess what data sources are available, the quality of available data and what team resources will be required to make the data useful. Also, you may discover you need support beyond your existing team.

Note: many companies start with a Proof of Concept to validate that the results will be enough at scale to warrant the required investment. Quantifying the potential opportunity up front is critical to sustaining interest throughout the process.

Ingredient #2: Know the Specific Analytics Skills Needed

Even the definition of a Data Engineer (DE) and a Data Scientist (DS) can be confusing; specifically, defining these two roles and how they are different than a Software Engineer (SE).  You can read the differences between a DE and DS in this O’Reilly Media article, Data Engineers vs. Data Scientists.

GAP considers the talent and their responsibilities in this way:

  • The DE solves the technical challenges of merging disparate sets of data into a common taxonomy for use by the DS and SE.
  • The DS finds actionable insights using modeling, machine learning, algorithms and statistical analysis to help surface patterns and trends to meet the business objectives. They have math, science and statistics backgrounds and, while technically savvy, they are not programmers.
  • The SE architects builds powerful software applications that bring to life the work performed by those working with the data, so that the end user can now leverage the data in ways to meet and deliver the business objectives.

We would be remiss to not state the obvious – these individuals are in demand and hard to find.  For example it’s estimated in this Infoworks.io article DE in Greater demand that DS that you will need multiple DE’s for every one DS. 

We have identified three possible ways to gain the required data analytics talent starting with Data Engineers:

  • You can direct hire: Assuming you can find them and afford them. According to Glassdoor, the average salary for a DE ranges from US$81,000 to $160,000 depending on the specific skill set required.
  • You can outsource from an offshore location in Eastern Europe, Asia or India: assuming that you are comfortable with addressing obstacles or outcomes outside your working hours, sometimes with language barriers.
  • You can use nearshore resources to complete your team: companies are finding success working with distributed and affordable data analytics talent, such as Data Engineers in Latin America.

Ingredient #3: Know the Potential Investment Required

There can be some nice surprises with data analytics. When evaluating the required investment in time and money, the investments can be lower than expected – and well-worth it.

  • Some assume data analytics is costly, limited to big budgets and big engineering teams. We find this is not entirely true. While you need a good understanding of your data, as well as what business objectives you are trying to meet, your largest investments will be finding and hiring the necessary professionals required.
  • When you balance the right skills mix and an appropriate development methodology, such as Agile, insightful results many only be months away.

Not unlike other products/projects, the true investments are reflected – and more quickly realized – if intentional actions are used to pull together the necessary people, process and technology. 

SUMMARY

In this blog post, we’ve outlined the need to break down the complicated parts, including sub-components and processes that have some logical and practical order. We’ve given you some key definitions of what data analytics means to GAP. Lastly, we’ve looked at the three ingredients to baking the perfect data soufflé. We have seen how knowing our business objectives helps us identify the right skill set and required investment to leverage data analytics.

Here at Growth Acceleration Partners, we have extensive expertise in many verticals. Our Centers of Engineering Excellence (COEs) in Latin America focus on combining business acumen with the highest calibre of technical knowledge to help your business.

We can provide your organization with a team of highly qualified data scientists, engineers, mobile developers and consultants who are highly skilled in:

  • data analytics
  • data science
  • information systems
  • machine learning
  • predictive modeling
  • software development

If you’d like to find out more please visit our website here. Or if you’d prefer, why not arrange a call with us? You can catch us on email if you’d prefer.