Digital Enterprise



Enterprises have embraced digital technologies for many years, but today we find that to be more true than ever.  What is a digital enterprise?  Briefly, it is a way of doing business that includes the varied and integrated aspects of the latest digital technologies to conduct business more effectively and at a lower cost.

The new digital enterprise forces companies to take a new look at how they conduct their business.  First, companies must be willing to let go of traditional digital models and morph into ones where they may have less direct control than before.  For example, a traditional accounting system with software on a desktop can be replaced by a SaaS based system in the cloud.  

The technology with the new digital enterprise is actually a stack of technologies which must be carefully integrated together to provide a trusted, viable application.  A general view of such a system is shown below:

The Remote Node can be something as simple as a temperature or pressure sensor or something more complicated such as a heart monitor or blood flow monitor.  

Gathering data is only the beginning.  The data can then be sent to the Cloud, or contained within the enterprise (either physically [sometimes called ‘on-premise’], or virtually in a private data center).  Various tradeoffs of reliability and storage/support cost factor into the cloud/on-premise storage and compute decision.  Most manufacturers today are examining a hybrid model; with some immediate floor-level activities being on-premise, and higher level systems (ERP, Supervisory Shop Floor control, etc., ) cloud hosted.

The advent of very inexpensive storage and compute has also spawned new evolutions in areas of big data (typically Tera or PetaBytes of information), Analytics (transforming Big Data into actionable knowledge), and Artificial Intelligence (AI). Today many of these are being utilized by advanced digitally transformed manufacturers, and are intense areas of experimentation by many.

Security in the cloud is always an issue that must be addressed. Application and data may reside on servers and in a storage devices that are shared by many other applications and data. How this data is segmented and protected is critical to ensure end-to-end data security.   In addition, server uptime guarantees, as well as network uptime, latency,  and bandwidth are all critical attributes that must be part of any Service Level Agreement (SLA).  


With today’s rapid changes in computing and networking costs, now is a critical inflection point for the digital enterprise.  Never before has compute/storage/networking performance and cost changed at the rate it is today.  This provides challenges and opportunities for the digital enterprise  to embrace and harness these changes to enable changes to their operations that are akin to changes that occurred during the last Industrial Revolution.

The somewhat recent introduction of the Cloud and Cloud services to the digital enterprise has made dramatic changes to the finance, support, and use of computing.  The rate of adoption is expected to continue to grow at 15-20% per year. According to the International Data Corporation (IDC), cloud IT infrastructure spending will grow at a compound annual growth rate (CAGR) of 15.1% and will reach $53.1 billion by 2019 accounting for 46% of the total spending on enterprise IT infrastructure.  

The proliferation of cloud offerings -- SaaS, Iaas, PaaS -- that give IT professionals an array of options are certainly driving increasing interest, as are the obvious financial appeals.   It allows organizations to shed at least some of their expensive IT infrastructure and shift computing costs to more manageable operational expenses.  The cloud also eases much of the technological burden involved with IT systems support and maintenance, helping companies focus on the productive business use of their workloads rather than on underlying systems and software.

Closely coupled with the growth of the computing and storage cloud arenas are the growth and utilization of Big Data.  Although the moniker of Big Data has existed since 1999, only in the last 5-7 years has the storage and computing power to look at gigabyte and terabyte data sets in almost real time been readily available to users.  Although interesting as a technology trend, Big Data only has real business value  as a capability that allows companies to extract value from large volumes of data. Like any capability, it requires investments in technologies, processes and governance. The research firm IDC forecasts that the revenue from the sales of big data and business analytics applications, tools, and services will increase more than 50%, from nearly $122 billion in 2015 to more than $187 billion in 2019.  The analytics portion of this number is very significant and becoming more so, as commodity pricing will continue to drive down hardware cost--and the value will reside in the business application of the analytics.  

Significant improvements have been made in the analytics tool space, enabling easier/faster/better analysis on many different types of data (transactional, social, interaction, etc.)  The rise of the Data Scientist in many different fields/verticals has also resulted in a more structured discipline around analysis; breaking it down into 4 primary categories or strategies:

  1. Performance Management
  2. Data Exploration
  3. Social Analytics
  4. Decision Science

Each of these strategies has has it’s own tool sets, and can be used by businesses to optimize existing processes, develop new processes/products, develop new selling strategies, as well as a host  of other value added actions which can be fact-based, rather than intuition or ‘shooting from the hip’.

The role of Artificial Intelligence (AI) has begun to play a larger role in many of the analytical strategies described above.  Although the term and nascent adoption has existed for years, only  within the last several years has AI been a seriously promising field.  Technology companies are moving swiftly to create and capture value in this emerging area. High-profile technology acquisitions are piquing interest in AI  technologies such as robotics, expert systems, computer vision, and speech, gesture and facial recognition. Many companies are creating new research labs devoted to innovating with these technologies, and the number of Artificial Intelligence vendors has increased dramatically.

Interested companies must also recognize that AI isn’t a matter of any single technology or application—whether driverless cars or smartphone virtual assistants or trend detection solutions
or a myriad of other examples. AI  is a rich and diverse field. The greater value will come from understanding the multitude of related technologies and then integrating those technologies into
full solution.

Data Security is a holistic issue for all of the aspects of the Digital Enterprise.  None of the advances and promises of any of the afore-mentioned areas will be useful if they are not secure--from data collection to data storage to data analysis.  To unleash the power of business through technological innovations, ensuring data protection is paramount . This means protecting your devices, your network, data center, and the cloud. Highly granular data security protection and well defined business processes are essential to accomplish this goal.

The selection of the appropriate strategy, and selection of the appropriate technologies will be the key to the success or failure of enterprises making these revolutionary changes.  How the insertion of Cloud, Big Data, Analytics, AI and Security into each digital enterprise is managed will be a key differentiator for success.

Artificial Intelligence

Big Data / Analytics



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