Welcome!

Java IoT Authors: Liz McMillan, Elizabeth White, Yeshim Deniz, Zakia Bouachraoui, Pat Romanski

Related Topics: @DXWorldExpo, Java IoT, Mobile IoT, Linux Containers, @CloudExpo, SDN Journal

@DXWorldExpo: Article

The Non-Analytics Company Is History

The fundamental mechanics of business have changed and the non-data centric company will ultimately be history

The fundamental mechanics of business have changed.

Well, they haven't quite.

The basic laws of supply and demand still govern the economic principles inside which firms in all industries bring goods and services to market inside a common monetary system on an international level.

But a change has occurred and it is an information-driven shift.

Our core accounting systems used to represent the motherlode of all company information. Onward from there... somewhere around the end of the last millennium we added so-called Customer Relationship Management to the corporate information arsenal and started to build up the commercial data bank.

Fast forward into the first decade of the new millennium and we found ourselves deeply entrenched (and enamored with) the world of Enterprise Resource Planning (ERP). In the ERP-enabled world we started to define Key Performance Indicators (KPIs) and use business metrics in a more mathematically sensitive way than ever before.

What makes a truly data-centric firm?
Today we take ERP as a given element of a wider total corporate data stack. The modern firm captures data from accounts, from customers, from business units (in the ERP sense) of course, but that's just the start. A truly data-centric firm also captures information from employees, external competitors, business equipment (in the capital expenditure CapEx and operational expenditure OpEx sense) often with Internet of Things style sensors and more besides.

To clarify our argument one crucial step further, this (above example) is not a truly data-centric firm; this is only a data-aware firm. A truly data-centric firm is also capable of capturing these multi-level information streams and being able to analyze them for operational (and so therefore) commercial advantage.

Future investment brokers won't ask to see profit and loss statements; they will ask "how good is your Big Data information capture and analytics procedure system?" or such like. Okay yes they will ask for P&L too, but you get the point.

This practice of analytics is defining the modern 21st century business. Knowing what customer movements mean is important, but knowing how to analyze what connected ancillary factors will influence customer behavior before it happens is what really makes the difference.

This overall trend for change toward analytics has certain effects. Firms need the same mix of salespeople, IT, finance, admin and other staff; but now they need a defined specialist to serve as a Data Scientist (CAPS intended to denote job title) -- or, at least, they need to be able to outsource the consultancy services needed to supply that analytics intelligence.

Patterns and anomalies
Companies who "get" the analytics challenge are using a variety of tools to surmount and conquer the Big Data challenge. The data scientist (lower case from herein) is using elements such as the Apache Hadoop open source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware. On top of Hadoop the data scientist is using In-Database and Hadoop In-Memory Analytics to start to uncover patterns and anomalies to get new insights and make decisions based on facts discovered.

The data scientist uses Data Vizualization tools to (in theory if he/she does it right) begin to uncover patterns in both internal and external data and start to perceive and act upon the resulting analytics at hand. These same analytics tools can of course be turned inwards so-to-speak and focused on the firm's own operations to uncover trends and perceive and predict actions that could and should be taken to maximize profitability and welfare of employees and customers.

Analytics used at its most effective level becomes a tool for firms to drive their ability to compete and innovate.

Nobody's perfect (with data analytics) yet
This is the pure (as opposed to applied) theory of data analytics where it sits in perfect post-deployment harmony inside a Hadoop (or other) managed Big Data framework. Not all of this theorizing is easy to pull off over night and we know that Hadoop installations are complex by their very nature. But taking the purist pure view is a good exercise to undertake at this comparatively still early stage for cloud, Big Data and analytics (and let's not forget mobile too). We need to discuss what is possible and then see how close we can get to perfect.

In this new world of business is it now fair to table our opening gambit again? Have the fundamental mechanics of business changed? For many real-world businesses today there is now an open admission and acceptance that data is the greatest commercial asset that they have. Not every firm has complete control of its data asset base, but this is precisely why we are having this discussion.

The fundamental mechanics of business have changed and the non-data centric company will ultimately be history. Soon after that, the non-analytics company will also be a distant memory. Senior management is (largely) agreeing with the need to shift resources toward data-driven decision making and wider Line-of-Business strategies are also falling in line.

A mindset for the future
The sophisticated data analysis innovator has fine-grained business control and a stable strategic growth path planned out that is capable of constant and continuous dynamic change. We're not re-inventing the light bulb or the wheel today, but we are re-inventing our core operational business ethos and mindset. Nonbelievers in the data revolution will be historical figures sooner than they think.

This post is brought to you by SAS.

SAS is a leader in business analytics software and services and the largest independent vendor in the business intelligence market.

More Stories By Adrian Bridgwater

Adrian Bridgwater is a freelance journalist and corporate content creation specialist focusing on cross platform software application development as well as all related aspects software engineering, project management and technology as a whole.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


IoT & Smart Cities Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...