Data Scientists Plus Business Experts Create Partnerships

Data Scientists Plus Business Experts Create Partnerships

By ellenjharris | Blog

Jan 01
Data Scientists

Data Scientists Plus Business Experts Create Partnership

Executive Management Perspective: Data scientists can capably bridge gaps with business experts.

Most Data Scientists are aware a gap exists between tangible output and business expectations.

Ten years ago few non-technical professionals knew much about email, smartphones, storage devices, data centers which predate cloud computing. Professionals today are more technically aware. They are savvy about basic technologies they use every day.

Business professionals better understand how technologies integrate. They are more clear how technology can serve their business objectives.

This discussion explores a dynamic between business experts and data scientists. Outcomes serve business environments where companies, business experts and data scientists harmonize around business goals.

First of all, there is a dilemma, a gap between business professionals and technologists. In many companies this gap is consistent. Communications and language use impact multiple business units where core conversations involve technologists as focal points. Comments will often sound like: “I wish we had a way to…”, “Why can’t we…”, “What if we…”, “Do you think we could…?”

This is especially relevant when there appears to be a penchant toward exclusion at risk of profit. In other words, while management wants solutions, too often professionals who should be included in decision meetings are left out. Many meetings which could orient a data scientist into company mindsets are not always open sessions.

Hence, there are many areas where data scientists may feel opportunities are not open to them.

Inside Data Science communities, entrants with computer science degrees are excluded from plumb opportunities. These opportunities are formed as strategy sessions where discussions translate forecasts into data variable of high value to scientists. Perceptions almost suggest these professionals are not physicists as suggested by Chief Data Scientists at a prestigious Fortune 200 company. Exclusion is not limited to particular sectors of the industry. It seems like this dilemma is not limited to specific segments of the business.

Business functions like marketing and sales unwittingly continue to foster exclusion in some industries. Despite training and credentials, there is a perception a data scientist lacks revenue-generating skills.  It may seem like a professional data scientist is unable to evaluate a potential consumer market. Furthermore, there is an assumption a customer as a candidate for a nurturing sequence is undefined. However to nurture potential customers is a team effort that includes technical professionals.

Analytics vs. Forecasts

Exclusions increase as critical meetings are scheduled on executive calendars. These meetings tend to exclude people who will perform critical work. What type of meetings does not include data scientist? A meeting on strategic forecasts, budget planning, and asset allocation.

Few marketing or sales meetings include a data scientist. Introducing Revenue Marketing saw more data scientists invited to work with marketing and sales. Discussions about touch-points, visibility, reach, customer engagement details saw an increase in team engagement. With data scientists in meetings competitive advantage grows.

Thanks to MarTech marketing science and sales merged. Best outcomes suggest team members recognize high value speaking with a skilled technologist. Professionals learn to adjust to their lane where they have expertise.

These meetings generate an expectation of data. Without inclusion, data scientists have issued directives without the context of a product, market, market share, share of wallet or ROI. Consequently, without insight and context data scientists may sense themselves working in a vacuum.

High-performance tools manipulating data have a value. Data points are a necessary function of a tool’s output.  In many cases, analytics are unclear without interpretation of a business context.  Results appear out of synchronization with business experts.

Measure for Success

In construction home builders working with wood or steel continually measure twice. Similar to construction professionals, data scientist marks each item with a pencil highlighting specific nodes for analysis. They measure for success.

Macro environments are massive with little tolerance for professionals who are not ‘up-to-speed’ on how business works, and, how it generates revenue. Keep in mind a mindset shift from employee to entrepreneur. Any former employee who transitioned to entrepreneurship will tell you there are two distinct mindsets:  employee which is a doer and entrepreneur which is the owner, negotiator.

For a data scientist, it is extraction with tools which manipulate unstructured data.

Statistical analysis, a high-level principal component analysis of raw data is where a coefficient equals PCA of (X). It results in loading ‘n’ by ‘p’ data. This, as computer scientists know, is matrix ‘X’ using MATLAB in testing. Yet it does not end there. It is natural language processing translating those interactions between humans and computers. It is a team which helps a data scientist define value variables of ‘p’ in order to make data make sense.

Data Scientists measure output against macro business expectations. This is paramount. There is a need to measure twice to produce an accurate forecast. Data scientists measure with precision, ‘x’ number of touch points required to attract prospects into funnels which converts to sales.

Done correctly data scientists will be within a Fermi solution of plus or minus two. Because of multiple translations between business experts and machine language partnership is critical to success.

Define meanings and work around

Since essential business relies on data, how does a data scientist get to revenue from here? Try this.

Because data scientists work in macro environments it is a good idea to read management discussions found in a company’s annual report. A 10K is an annual report. Read two previous years. Focus on expectations for a current year you are in.

If a company issues an 8K peruse that document as well. It is an update of deviations a company took from an initial projection for a fiscal year.
What an annual report does for a data scientist is to provide a road-map of expectations versus actuals. In other words, an 8K is a forecast compared to what actually happened in a fiscal year.  As a result of this off-hour homework, a scientist will have a grounding from which insightful questions can be formed and asked.

In addition, it will put a face to data projects you work on. It will help you understand  ‘why’ due to a project’s intricacies. Maybe you can connect with those business experts around what they know best about business. Consequently, you will have created a positive working alliance to build better outcomes and results.

Especially relevant are massive volumes of data you manipulate which emanate from each and every strategic business unit in your company. Data along with your statistical analysis illustrates where you connect elusive revenue dots.  Touch-point nodes stimulate consumers toward making a purchase.
If your company is pursuing an emerging market strategy you will gain capabilities to anticipate particular projects with expected outcomes.

Interestingly you effort will tie you back to analytic requirements and business need. Furthermore, data takes on a sense of human qualities.  Since machines collect data how data is interpreted binds data scientists with the business experts. Because paying close attention to machine output guides you to solutions that appear innovative.

You discover you are in ‘the zone’ where technique and technology meet for competitive advantage. Another benefit set for businesses, customers and teams. It is a proverbial sweet spot where your deep technical skill meets your intrinsic value.

Intrinsic value helps you:

  • Couple your discovery with speed, ask right questions.
  • Immediately observe you have cracked core code.
  • Satisfy what a business wants.
  • Begin to embrace what you need as a professional data scientist.

Finally, this analysis is not meant to be complex. Most noteworthy is your insight to give you a place to start. As a Data Scientist, you can begin making business sense of your contribution in a macro environment.

Gaining access to key discussions is a challenge.  As a result of these suggestions here, you can expand your insight reading an annual report or 10K of a publicly traded company. This effort extends your perspective about data you work with every day.

Data Scientists will begin to construct a story behind data. Consequently, stories help position data into a context that includes a big picture of a company and markets in which that company operates.  Most of all a good source for macro industry information is reading what your company’s executive management reads. Peruse CEO express on your smart device.

Maybe, if you are curious chief executive provides you a higher level perspective. Also, you will achieve a depth of where and how to generate your data.
Probably, best discoveries help you uncover your intrinsic value. Almost every Data scientists can advance in their work. In conclusion, their career as a data scientist connects with them with their core values as Big Data specialists. While not immediately apparent data science is not only transactional but also relational.


About the Author

Ellen J Harris leads the Business-Accelerated Company. The firm is a C corporation specializing in Intellectual Property, Intellectual Capital and Intellectual Revenue Assets. Harris is a frequent guest on Blog Talk Radio, contributes to the NYU School of Entrepreneurship as a Business Consultant Coach in the $300K Entrepreneur Challenge and serves on the Advisory Board at the Lubin School of Business at Pace University. In her current portfolio, Harris leads three local U.S. Startups and one international startup launching in the second half of 2018. She serves small businesses in a variety of industries. Harris helps business owners maintain their compliance with federal requirements and most recently compliance with the European Union's General Data Protection Regulation.

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