Modern Data Science

Businesses looking to understand the internal and external business factors that determine revenue and profitability are looking more at Big Data sources and Data Science than ever before.  Customer and prospect behaviors and buying patterns are key to the Modern Data Science push.  Recently, we have also taken a strong interest in Modern Data Science, contrasting it with our Lean Six Sigma (LSS) areas around the use of statistics and exploring how to leverage the two bodies of knowledge for Advanced Business Process Analytics.  The field of motorsports is also advancing Modern Data Science techniques with sensors and machine data that will become commonplace in major data warehouses.  Amazon is an outstanding example of a company that we believe integrates many of these areas successfully in day-to-day business operations and activities.

Below is a graphic that shows Alexicon’s view of Modern Data Science, including Big Data, and the important areas for overall company success:


We found that business management, LSS, and Modern Data Science complement each other individually and together as a learning platform.  All areas can be coordinated and used efficiently and effectively by large enterprises today, from strategy to the store or shop floor.

If you are working to meet the next quarterly goal or looking for strategic or future solutions, we can help.  Don’t just survive; thrive in the Modern Data Science era and welcome the Digital Age by getting your company familiar with a cohesive enterprise approach to advanced business analytics.  With the current hotbed of rapid technology innovations in Analytics and Modern Data Science, major corporations are challenged with seamlessly integrating these major methodologies for performance improvements.

Modern Data Science, Business Management and Lean Six Sigma (Integrated Enterprise Approach)

Managing several analytic software tools with limited in-house expertise can slow analytic advancement and online service demands by company users.  A comprehensive analytic strategy is needed to utilize the best mix of tools with an overall plan for the entire system, including databases as the focus.  Handling business requests for more visualizations or data sources is an ongoing process that requires new integrations in the enterprise data model or landscape.  This is where we believe our cross-methodology experience will enable us to provide the correct guidance to clarify the current progress, direction, and benefit you expect or envision from spending project dollars around Advanced Business Analytics.

Modern Data Science

Today, Modern Data Science stands on the shoulders of LSS and traditional Data Science for statistical uses and goes much further with big and rich data.  Modern Data Science uses regression and prediction, classification, and hypothesis testing, as well as adding deep learning and methodologies for "recommendation systems."  Some people see these as modern Artificial Intelligence (AI) systems.  Early advances were made by Google pioneers for Hadoop while managing a world-class internet search service at scale.  Facebook has been key for advanced social enablement, data collection, and recommendation engines.  Our world has advanced rapidly with Big Data and Modern Data Science because of these commendable and early efforts.  Data Science is an emerging area of study for many college programs.


"Data scientist" has also become a popular occupation with Harvard Business Review dubbing it "The Sexiest Job of the 21st Century"

There is also room for maverick data discovery with Modern Data Science. Maverick creative finds can benefit their Company’s quickly.  Our structured approach allows for creativity at its highest forms while capturing structured steps for quick production uses when discoveries are found.

"Data Science Process" diagram (Wikipedia) - Decomposition (orange points)

Below is the process shown in Wikipedia for Data Science.  You will notice that steps 1 through 3 are traditional steps for the Extract, Transform, and Load (ETL) process for Enterprise Data Warehouses (EDWs).  Data Science can include formal or end-user informal ETL.  The orange circle numbers and notes were added by Alexicon to demonstrate our understanding and focus on embedding learned know-how from Data Science activates (4) into the formal (1, 2 & 3) ETL process and/or including them in the database layer (3 & 5) as embedded Models & Algorithms for runtime computations.  The EDW will end up having a “clean dataset” or table(s) and algorithms or computation codes for database use.


"From the business perspective, data science is an integral part of competitive intelligence, a newly emerging field that encompasses a number of activities, such as data mining and data analysis."

Source: wikipedia.org/wiki/Data_science

In-database Computations Are Better and Faster

The place for proven computations is in the database (not client desktop tools).  Algorithmically efficient computations are important with large data sets.  They must also be meticulously vetted for accuracy.  Users expect response times from newer databases in under two seconds versus many seconds or minutes from traditional big reporting systems.  There is still the need to balance summary and detail levels.  The goal is to move computations to run in the database at super-fast speeds, with summary or aggregate results sent to the visualization layer or report.  Smaller result sets from the database are easier on the network, as well.  Fast query results help BI Tool Users by providing quick access to get what they want when they need it.  User and analyst time typically runs at a premium.  Many BI systems are a learning hub for a company by fostering user exploration and use (speed is important).  Highly used systems provide great return on investments (ROIs) across the income statement (P&L) and balance sheet over many years.

High-dimensional Statistics

Advanced uses in massive multidimensional spaces is one of our key focuses.  These new systems consider many text and numeric values, including where you live, your likes, your dislikes, your gender, and many other aspects of your publicly available information or “consumer data.”  There is also an abundance of Worldwide Government and Business Data.  Common usage areas for data are financial, geographic points and areas, weather, and Industrial IoT (sensor or machine data).  We see a strong relationship between multidimensional BI and high-dimensional statistics.  Both potentially have many dimensions and many numeric values in one related or analyzed schema on conformed dimensions or matched data to relate and contrast different measurable aspects of a business.

Modern Data Science makes possible newer and valuable capabilities like recommendation engines.  We see these recommendation engines in play when shopping online with similar product recommendations or ads being displayed while we browse or shop.  These engines are capable of understanding and associating people to products or people to people because of high-dimensional statistics.  This is the big win! It is difficult for people to see and comprehend more than two dimensions (it is too complex for the human mind).  We typically use a two-dimensional (2D) space or plane or X,Y plot to correlate or associate variables.  Moving from two to five dimensions gets difficult, not to mention the tens to hundreds of dimensions and/or numeric values that exist in Enterprise and Big Data environments today.

We are doing things today that were never before possible because of high-dimensional statistics.  This is where Modern Data Science and Machine Learning capabilities have taken a quantum-leap in recent years around prediction within massive data sets, which have data with high volume, variety, and velocity (the three V’s).  This creates complex and fast-moving or even streaming data environments, where Modern Data Science is used as ETL to transform and load data on the fly and/or perform real-time actions.

Data that is classified correctly allows users to contrast and compare different aspects of the enterprise with advanced business analytics capabilities.  We could draw a resemblance between Modern Data Science and radar when it was originally introduced.  Radar was used for military purposes to allow our forces to have long-range visibility even in darkness and fog (humans could do this without radar).  This is much like high-dimensional statistics and finding needed or important information in massive multidimensional spaces without the machine’s help to see what is not humanly possible otherwise.

Recommendation systems have fostered modern advancements with high-dimensional statistics through deep learning and other techniques used by early adapters like Amazon, Netflix, Yelp, Pandora, and Tinder for online use and matching (recommending) people to products or people to people.

Sensors, Machine Data and Performance

Similar to businesses, the field of auto racing uses Modern Data Science to increase performance and is actually advancing the use of sensor data.  Many race cars now have many more sensors (motorsport data acquisition) to collect data, which creates Big Data that needs to be analyzed for performance gains.  We believe businesses will follow motorsports with the use of more sensor data to monitor operations.  Below are heat sensors on a AA/FC Funny Car (Drag Racing) that have led to supercharger and fuel mixture changes for added horsepower (hp) and performance.  Analytics gathered from these sensors have made significant contributions to modern drag racing performance.  These drag cars run nitromethane and methanol and produce up to 10,000 hp.  Heat exhaust sensors have been key for increasing performance with these cars. The same is or can be true for industries that use sensors or machine data heavily.


Sensor data is gaining popularity with major corporations as well, who integrate that data with financial and operational data - for example, oil and gas corporations.

Business Management

Since our founding, we have heavily used business management methodologies in BI systems (19 years).  This also includes the use of advanced calculations and some use of statistics or computations around regression, prediction, and classification areas.  We believe all business areas highlighted in orange below present opportunities today for most major corporations to utilize Modern Data Science.

Usually, statistical techniques and methods are handled outside BI systems with external applications (or Excel), and then the resulting data is piped back to BI through the data warehouse or joined in the frontend BI tool.  Many times, data for the external tool is even taken directly from a BI download.  Much of our work involves moving this work to advanced backend data models while preserving the cutting-edge exploration and discovery work done by valuable analysts using a variety of frontend tools, including Minitab, SAS, MS Excel, and others.

Lean Six Sigma (LSS)

LSS gives us process-based statistics for organizational management change and system changes.  There is a heavy use of regression, prediction, classification, and hypothesis testing in LSS. A good example is “design of experiments” (DOE) or controlled tests to predict accurate outcomes by introducing changes in settings or parameters to tune the desired outcome.  This is different than “training data” with Modern Data Science; however, it shares the same concept (tuning outcome accuracy). 

We feel that all of the LSS statistical areas below can be used equally at scale with Big Data sources and with Modern Data Science and that Modern Data Science will evolve to use many LSS methods and techniques for process-based monitoring and detection, as well as many other improvement uses.


LSS uses control charts to monitor variation in processes to identify things that are out of control or out of the norm (variation).  These systems are particularly useful with larger deployments, helping users spot and deal with unwanted variation within high data volume and velocity environments (control limits are also used in databases as triggers for alerts and/or automated actions).  LSS systems provide clarity around Root Causes for undesired occurrences and how they are resolved.  Many companies share quality data at some level with all users in the enterprise to foster organization communication on issues or problems.

LSS fosters company-wide performance with proven process-based improvement techniques for major and smaller corporations.  It is a toolbox for senior managers looking to improve the way a company operates through coordinated Change Management.  Our focus over years has been to integrate LSS with Business Management methodologies to add and promote measurement-method knowledge, user visibility, and hands-on Root Cause identification and ways to resolve problems.

Amazon (integrated example)

Amazon is a fine example of a company that sees the use of Big Data, Modern Data Science, and Lean Six Sigma (LSS) for internal operations, website operations, product recommissions, and taking orders.  We believe there is much crossover that occurs within a company like Amazon with these bodies of knowledge.  They have mastered the management of process and data so well that they offer Amazon Web Service (AWS) so other companies can take advantage of their advanced system infrastructure and architecture to store, compute, and analyze data with advanced capabilities.

Alexicon is committed to helping customers increase enterprise performance capabilities with advanced business analytics using proven Business Management methods, Modern Data Science, and LSS.  We believe this combination is unique and valuable to all companies when integrated and coordinated (integrated techniques and methods).  This is especially true for major corporations with initiatives to increase sales and/or profit while controlling costs.

The time to get involved in Modern Data Science is now

Your company can start with an "Enterprise Analytics Health Check" and a review of known external and internal data sources or data landscapes to identify where Modern Data Science capability can be used to provide added analytic power for your enterprise.

In our mind, Modern Data Science will be integral to the success of major corporations in the coming years, as competition increases. What’s more, with new opportunities that are coming down the pike for American businesses, Modern Data Science will provide the ability to ramp up operations to meet that new demand and ensure that businesses can corner the best possible portions of the market before new entries or competitors take that early and important share.

Contact Us to learn more about Modern Data Science










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