Your big data projects are being hampered by the war for talent. But don’t feel bad, you’re not alone. Everyone in the race to monetize data has this same problem.

The highest performing firms in data and analytics only do about half of their work internally and outsource the rest. Since we supply both software to their teams, along with outsourcing tech enabled services, we get to know many of these organizations. We’ve come to know them very well, in fact. They have allowed us access to their data and their difficult business decisions.

Because of these high-trust relationships, at some point the client’s executives will confide in us one of their biggest problems: people. They will often say, “we can’t even get (X) to come and interview.” This X might be “data scientist” or “AI engineer” or “data analysis expert.” In our experience, the size of the company doesn’t matter. And frankly, the business you are in doesn’t matter much either.

We hear this same problem from clients of all sizes and stature. Microsoft has the same problem in the war for talent that your company has. Even firms which have exceptionally large cadres, still have a shortage of people and a severe quality people problem.

So, what can you do about your people problems? We’d suggest three things.

First, we suggest you think hard about what you want to do yourself. There is plenty of research to show that companies who perform well in data science, analytics, AI, and big data, are those willing to outsource some things while keeping a core team focused on others.  Learning to divide and conquer could be key.

If your internal team thinks they can do it all, this is a bad sign. In fact, it means you certainly have a people quality issue.

That leads to the second thing we’d suggest; embracing best practices to promote higher quality work from your people, and higher confidence results from your data and analytics.

It may not be their fault if your employees are falling short of best practices. The field is still too new to have best practices taught in schools. To make matters worse, it’s likely your teams are being overworked with little time to upgrade their lacking best practice knowledge. If this is the case, it partly management’s fault. This is a fast-changing field and your team needs some time to keep up with developments, like best practice research.

Lone Star Analysis led an international benchmarking project to determine best practices in these areas. We are happy to come to your organization to provide training for your executives who consume analytics results, and, for the teams who produce them. From time to time, we also offer this same training at our headquarters in North Dallas.

The third thing we’d suggest is to embrace no-code tools. Lone Star’s no-code solutions mean you don’t have to choose between domain knowledge and data science. We have fully equipped our customers with powerful analytic solutions they can use, without the need for Data Science PhDs. In return, they save those precious assets for the cases where they are really needed.

We believe with a focused effort guided by best practices and user-friendly tools, companies can now face the war for talent, armed and ready for battle with winning solutions.

About Lone Star Analysis

Lone Star Analysis enables customers to make insightful decisions faster than their competitors.  We are a predictive guide bridging the gap between data and action.  Prescient insights support confident decisions for customers in Oil & Gas, Transportation & Logistics, Industrial Products & Services, Aerospace & Defense, and the Public Sector.

Lone Star delivers fast time to value supporting customers planning and on-going management needs.  Utilizing our TruNavigator® software platform, Lone Star brings proven modeling tools and analysis that improve customers top line, by winning more business, and improve the bottom line, by quickly enabling operational efficiency, cost reduction, and performance improvement. Our trusted AnalyticsOSSM software solutions support our customers real-time predictive analytics needs when continuous operational performance optimization, cost minimization, safety improvement, and risk reduction are important.

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