Data science allows your organization to extract business value out of Internet of Things (IoT) environments.
Make data scientists your go-to resources for IoT insight.
According to Wikipedia, data science is an interdisciplinary field, involved in creating processes and systems used to extract knowledge and insights from various forms of structured or unstructured data. Data science includes, but is not limited to, the fields of statistics, predictive analytics, machine learning and data mining.
You do not get a free pass out of this conversation because your organization currently does not employ data scientists or even have a digital transformation strategy.
Let’s explore the advantages of discovering who these folks are. Get ready to get busy putting them to work on behalf of your organization.
Data science liberates everyone from spreadsheet mindset.
Is the majority of data in your organization paper- and spread-sheet based? Here is your IoT wake-up call.
Spreadsheets are static, historical and retrospective documents. They tell you what happened instead of what will happen next.
Big data is characterized by volume, velocity, variety, veracity and value. Data is generated at light speed within smart businesses, buildings and factories. Data scientists work with other teams to analyze data and create and test hypotheses leading to product, process, and systems improvements.
Make sense so far?
Integrating data science and data analytics platforms catalyzes digital transformation, no matter what size organization you lead. Developing a data-driven, digital organization not only captures data from various sources, but integrates it with data from other systems.
Extracting insights from big data continuously connects you to the big picture.
Big data analytics platforms are the basis of the data science toolkit used for data extraction, analysis and applications development.
Let’s demystify for the business folks reading this post.
Analytics platforms are used by data scientists, applications developers and systems operators to extract insights from large volumes of data (big data) generated within IoT environments. Typically, these high level platforms are available as a commercial Platform as a Service (PaaS) or in an open-source format. It all depends on the types of questions you ask and the problems to be solved. And your budget.
Open-source software (OSS) is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose. Open-source software may be developed in a collaborative public manner.
For example, the high-level, fully-loaded GE Predix Cloud platform created for the industrial Internet of Things ecosystem is a platform built on Cloud Foundry that supports multi-tenancy. In Predix, you also have capabilities to build your own apps as well as your own microservices and ‘sell’ these to the marketplace (much like Apple’s App store). Then there’s the cost structure. With Predix, GE runs and manages the hardware, network and platform for you. So with Predix you pay as you use those microservices.
Trusted Analytics Platform (TAP) sits at a lower and more affordable level than Predix. The TAP platform does not have built-in microservices. Instead, its multi-tenant, open-source format promotes collaboration. The beauty of TAP is that the platform easily and flexibly allows data scientists to collect and analyze data and develop specialized applications across multiple languages and protocols. The biggest difference between TAP and Predix is that TAP allows you to run and manage your own instance (installation). There is an associated cost structure for running those instances (e.g., number of servers required per instance, etc.), whether on-premise or in the Cloud.
To execute a digital transformation strategy, hire data scientists or contract integrators.
I spoke with Tipton Loo, Vice President, Analytics, at ProKarma, an integrator and developer utilizing various analytics platforms for multiple industrial IoT environments. I asked him why companies turn to data integrators and developers for their IoT journey.
Loo urges you to “look at all of the manual tasks you do today which involve lots of data points.” A digital transformation strategy involves various analytics platforms which “free up operators to do other work where they are more needed versus recording data.”
Loo asks you to think about all of the daily manual tasks performed in your organization, especially those involving the supply chain or operations. The setting can be on a factory floor, in an IT department managing network/hardware, or on an oil rig, etc.
These manual tasks involve humans walking around and recording machine information or inventory. People are inconsistent and highly variable. Operator error is the root cause of incomplete, hand-written data entry, which often is illegible and easily misinterpreted. As a result, inventory can be misplaced, resulting in over- or under-ordering. Equipment can fail when it is not supposed to.
Loo cited the Levi Strauss & Co. business case, in which the company used Trusted Analytics Platform (TAP) in a proof of concept scenario to focus on locating misplaced items. Based on the success of the pilot program, the same solution can be adapted to help retailers gain greater front- and back- end control of the supply chain for per store stock replenishment.
In addition, Loo is exploring the TAP platform for industrial IoT, especially involving brownfield software development to retrofit or bridge legacy software systems. Getting legacy IT software systems to communicate with each other can be difficult and time-consuming. With TAP’s ability to function across various software languages and protocols, “this is no longer a barrier to adoption,” according to Loo.
Also, Loo and his data science team used TAP to develop predictive analytics for an open source healthcare reference architecture. The goal was reducing readmission rates in hospitals. Currently, Loo is working on another TAP-based, proof-of-concept healthcare project to identify patients at risk for rapid decline. The system would trigger a rapid response team.
Move your organization from being retrospective and reactive into becoming data- and insight-driven.
Loo is sold on the value that TAP brings to the organizational table. “TAP brings value not only to the data scientist. The platform increases speed and ease of collaboration with doctors, plant engineers and other teams throughout the enterprise. With the readily available machine learning algorithms, data scientists can quickly extract results and confirm with these experts.”
Perhaps the only barriers to digital transformation involve fear of taking that first digital step forward.
Walk through your organization. Observe the number of manual tasks performed by employees which might be more accurately recorded digitally. Consider the impact that digitizing 10% of these manual processes can have on overall efficiency, performance and operating costs. How about 20%?
Which departments will you target as ground zero for your digital transformation project? Which data scientists and organizations are best-suited to work with your organization?
Babette Ten Haken writes, speaks and coaches about customer success for customer retention in the industrial Internet of Things (IIoT) ecosystem. She traverses the interface between human capital strategy for hiring and developing collaborative technical and non-technical teams. She serves manufacturing, IT and engineering intensive companies. Babette’s playbook of technical / non-technical collaboration hacks, Do YOU Mean Business? is available on Amazon. Visit the Free Resources section of her website for more tools.
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