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 and savvy about basic technologies they use everyday.
Business professionals today better understand how technologies integrate and work together to serve their business objectives.
While this discussion explores a dynamic between business experts and data scientists consider results which can serve business environments where companies, business experts and data scientists can harmonize around business goals.
First of all there is a dilemma of 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. While management wants a 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 industry. It seems like this dilemma is not limited to specific segments of 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 candidate for a nurturing sequence is undefined. How 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 do 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 are issued directives without context of 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. Like 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 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 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 for you where you connect elusive revenue dots with touch-point nodes that 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 the data scientist with the business expert. If you pay close attention to it you will begin to see solutions that appear innovative when you both are working together in tandem.
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 will help you:
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 what is suggested here, you can expand your insight reading a 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. As a result, that story will 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.
Ellen J Harris leads the Business-Accelerated Company which specializes in Leadership, Intellectual Capital, Intellectual Revenue Assets, Intellectual Property. Ellen appears on Blog Talk Radio, Scott Golden's Power of Perception and Hugh Liddel's Sales Talk Radio. Listen in as she shares insider tips on business growth.