As expectations around software delivery and big data continue to evolve, data analytics has taken center stage as an umbrella term for disciplines that allow companies to glean valuable insights from their data; disciplines such as AI, Machine Learning, and Predictive Modeling. Due to its inherent complexity and variety, as well as its relative novelty as a tech world heavyweight, analytics is still shrouded in some confusion. Most business leaders understand that analytics can help them turn their data into profit, but beyond that, the path is fraught with questions. How do I integrate analytics into my business? What kind of professionals do I need to perform these roles? What is the financial and time investment that I will be committing to? Though answers to these questions will vary by project, we wanted to provide some clarity into the possibilities of data analytics, and offer three keys that you can use to begin unlocking its power. 

Key #1: Define Your Specific Business Objective

Analytics is not a one size fits all discipline. Depending on what you seek to learn or accomplish, your team and methodology may look quite different. Clearly defining the business objective, and placing appropriate parameters around that objective, is the first key to unlocking the potential of data analytics. Here are some objectives that companies attempt to address or accomplish with analytics: 

• Improve profitability
• Automate business processes
• Make better investment decisions 
• Retain specific customers 
• Target specific prospective clients 
• Fight fraud
• Mitigate risks

Once you have defined your business objective, you will work to determine how a complicated range of data can be aggregated and manipulated to deliver exactly the insights needed. You will need to assess what data sources are available, the quality of that data, and what team resources will be required to leverage your data successfully and profitably.

Download our Analytics Case Studies to see examples of how GAP analytics addresses issues related to data quality and disparate data sources.

Key #2: Be Prepared for the Potential Investment Required

There can be some pleasant surprises associated with data analytics. When evaluating the required investment of time and money, you can sometimes find them to be lower than expected – and well-worth it.

• By default people assume that data analytics is always costly, and limited to big budgets and big engineering teams. In GAP’s experience, this is not always true. It’s possible to achieve success with analytics on a more modest budget, provided you have a good understanding of your data, and well-defined business objectives you are trying to meet. Your largest investments will be in the professionals required and the time allotted.
• When you balance time, the right mix of skills, and an appropriate development methodology such as Agile, insightful results may only a few sprints away.

Just as with all software delivery projects, true investments are reflected – and more quickly realized – when intentional decisions and actions are applied to put together the necessary people, processes and technology. Incorporating data analytics holistically throughout the business, rather than operating it as a separate group, will positively influence your organization on a broader scale.  

Key #3: Understand the Specific Analytics Skills Needed

Analytics projects differ from standard software development services in that they often require specialty skills, such as gathering data sources and mining those sources to extract, clean, transform and compile the data. As this process gets more complicated, and data amounts increase, the required talent and their responsibilities can be considered in this way:

• Software Engineers solve the technical challenges of merging disparate sets of data into a common taxonomy for use by the Data Scientists.
• Data Scientists find actionable insights using modeling, machine learning, algorithms and statistical analysis to help surface patterns and trends to meet the business objective (e.g. optimize profit, automate business processes, retain customers). They have math, science and statistics backgrounds and, while technically savvy, are not programmers. 
• Data Architects solve the continuity challenge by building the project roadmap and, together with software engineers, scientists and programmers, build a powerful software application to meet business objectives.

Analytics projects are typically carried out primarily by data scientists. These are highly competent technical professionals, who nevertheless do not possess the expertise of a software engineer. The performance, efficiency, and accuracy of an analytics effort and resulting software function can be improved exponentially by inclusion of software engineering in the development process. In many cases, GAP measures improvement to be 10 to 50 times over original client expectation by utilizing both data professionals and software engineers in our analytics practice. In real-world terms, data professionals are similar to architects, in that they design the structure, while the software professional is the contractor who lays a resilient foundation and erects a solid frame. One cannot complete a project successfully without the other, but working hand in hand, the two can build world class outcomes. This multidisciplinary collaboration is what makes GAP different, and consistently yields results surpassing client expectations.

We would be remiss to not state the obvious – all of these individuals are hard to find. When you are building a new analytics project team, there are three ways to obtain the required data analytics talent, each with benefits and drawbacks to consider based on the parameters determined by Keys 1 and 2.

• Direct hire: Assuming you can find and afford the required talent locally, you can onboard a new, full-time team member. GAP conducts ongoing research into salary trends for US-based software engineers and data professionals by geographic location. Contact us to receive a copy of the latest report.
• Offshoring or Outsourcing. You can outsource from an offshore location like Eastern Europe, Asia or India. While undoubtedly cheaper than hiring local, offshore comes with a host of potential issues like extreme time zone differences, language barriers, and cultural discrepancies that can create friction in the work environment and erode the process. 
• You can use nearshore resources to complete your team. Nearshore teams are based in Latin America, typically in countries close to the US; in GAP’s case – Costa Rica and Colombia. Our clients have found success with our distributed, nearshore teams in Latin America that operate on the Central Time Zone, have excellent English, and are comfortable working with North American clientele.

Conclusion

At GAP, we are very proud of our analytics practice. We challenge ourselves every day to raise the bar in lifecycle software delivery. This practice allows us to help our clients see through their business challenges – and craft unique solutions – by using volumes of their data proactively and effectively. In today’s data world, creating a new, clearly defined and verified algorithm is becoming increasingly rare. With that said, creative, inquisitive and curious teams can offer up methods of manipulating data that have previously been unexplored or were thought to be impossible. By building purposefully diverse teams, GAP has been able to provide our clients with context-sensitive methodology improvements that result in significant process optimization and automation contributions, to assist in deriving decisions and outcomes from the data sets. We don’t build diverse teams to fill a quota. We do it because we’ve obtained better results through research-derived cultural insights, and by following best practices to avoid bias in a consistent way, both a direct result of diversity of thought and expertise that’s hard-coded into our teams.

About Sergio Morales Esquivel

Sergio Morales Esquivel is the Global Analytics Technology Strategist at Growth Acceleration Partners, and a professor at the analytics post-graduate program at Cenfotec University. Sergio leads the Data Analytics Center of Excellence at GAP, where he directs efforts to design and implement solutions to complex data-related problems. Sergio holds a B.S. in Computer Engineering and an M.S. in Computer Science from Tecnológico de Costa Rica. Outside of work, he enjoys traveling, making games and spreading the love for open software and hardware. You can connect with Sergio on his website or LinkedIn, or send him an email.