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Business Data Explained: The Long Read

07 Saturday Oct 2017

Posted by Martyn Jones in 4th generation Data Warehousing, All Data, Big Data, Big Data 7s, Big Data Analytics, dark data, data architecture, Data governance, Data Lake, data management, Data Mart, data science, Data Supply Framework, Data Warehouse, Data Warehousing, pig data, The Amazing Big Data Challenge, The Big Data Contrarians

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data

Martyn Richard Jones, Gif sur Yvette 23rd September 2017

Integrity

Hi, Friends. As always, it’s great to be able to engage with you again. I am writing to you from the splendorous, wooded and verdant French town of Gif sur Yvette, and I wanted to take this opportunity to address aspects of data for an audience beyond the interests of data management and architecture people.

There is confusion about some of the fundamental aspects of business data, and there shouldn’t. If we strip away all of the boloney, it’s a subject that is quite approachable. In short, ‘it is not rocket surgery’. Continue reading →

Data-Less Apps: Revolutionary IT

09 Tuesday May 2017

Posted by Martyn Jones in 4th generation Data Warehousing, All Data, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, business strategy, dark data, data architecture, Data governance, Data Lake, data management, Data Mart, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Good Strat, Good Strategy, Good Strategy Radio, goodstrat, IT strategy, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians

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Image1Martyn Richard Jones

Dublin 9th May 2017

Weighed down with data?

Inundated with information?

Overstrained with knowledge?

Intimidated by wisdom?

Don’t despair!

Relief from all of this is just be around the corner, in the shape of the next revolutionary inflection point in IT. Yes, as you might have guessed it by now. I am talking about data-less apps. Continue reading →

Getting business value from data? Commercial analytics is where it’s at

29 Tuesday Mar 2016

Posted by Martyn Jones in 4th generation Data Warehousing, All Data, Analytics, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, Commercial Analytics, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, sentiment analysis, The Amazing Big Data Challenge, The Big Data Contrarians

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Analytics, Big Data, Commercial Analytics, Martyn Jones, Strategy

Martyn Richard Jones

Overview

I first became involved in commercial analytics in the eighties. First, through my involvement in customer segmentation and data visualisation, principally in banking but also in energy, manufacturing and the chemical industry. It also emerged later, in conjunction with my activities at the Sperry European Centre for AI, and was centred on pricing and yield management applications developed using a combination of statistical techniques, expert system technology and data centre architectures all tightly integrated within a 4GL development and delivery environment. This provided a comprehensive and seamless scenario building, hypothesis testing and reporting capability. Continue reading →

Big Data with BIG SMILES

23 Monday Nov 2015

Posted by Martyn Jones in Big Data, Big Data Analytics, Business Intelligence, Cambriano, Data Supply Framework, Martyn Jones, Martyn Richard Jones, Methodology, Process, SMILES, Strategy, Uncategorized

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Having got your attention I would like to introduce you to a pragmatic, real-world and business centric approach to Big Data and Big Data Analytics. When I say that this is the best approach to Big Data you are ever likely to find in the whole universe and in your entire life, I am still significantly understating the magnificent utility, timeliness and the here-and-now facets of the approach. Continue reading →

Operational awareness isn’t for wimps! So, get it right

08 Wednesday Oct 2014

Posted by Martyn Jones in awareness, Big Data, business, Business Intelligence, Data Warehouse, operational, pig data, Strategy

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Analytics, Banking, Behavioural Economics, BI, Big Data, Business, business analysis, Strategy

I frequently include the term Operational Awareness in talks.

I think it’s important for strategy.

So I wrote a piece that tried to convey what I mean by the term.

But first, a diagram:

Image1

Figure 1 – Operational awareness

This is a simplified high-level example of business data objects found in certain organisations. In the above diagram I have reused an industry example of nine business data objects to represent operational data[1].

For completeness and to maintain rationality in this section here follows a summary list of the nine key groups of business data needed to have a coherent and cohesive operational awareness (these data groups are also frequently referred to as business data objects):

  • A: Party embodies all of the participants that may have contact with the organisation or that are of interest to the organisation and about which the organisation maintains data. This includes data about the organisation itself; data about external organisations; data about external and internal individuals; and, data about the roles of involved parties.
  • B: Arrangement represents a prospective or existing agreement, between two or more individuals, organizations or organizational units that provides and affirms the rights, rules and obligations associated with a transaction between parties.
  • C: Condition describes the specific requirements that pertain to how the business is conducted and includes information such as prerequisite or qualification criteria and restrictions or limits associated with the requirements. Conditions can apply to various aspects of an enterprise’s operations, such as the operational parameters of a resource item, the sale and servicing of products, the determination of eligibility to purchase a product, the authority to perform business transactions, the assignment of specific general ledger accounts appropriate for different business transactions, the required file retention periods for various types of information kept by an enterprise and the selection criteria for a market segment.
  • D: Product/Service describes the services, merchandise or facilities that can be offered, sold or purchased by the enterprise, its competitors and other Involved Parties during the normal course of its business. This concept also includes goods and services that are of interest to the enterprise such as supplies for manufacture.
  • E: Location covers a place where something can be found, a destination of information or a bounded area, such as a country or state, about which the enterprise wishes to keep information.
  • F: Classification is used to organize and manage specific business information by defining structures that represent classification categories. Classification also organizes and manages groups of business concepts that apply to multiple concepts.
  • G: Business Direction/Organisation Direction refers to and records expressions of a party’s intent with regard to the manner and environments in which it wishes to carry out its business. Business direction items contains, keeps data about, and is used to support the enterprise’s business and financial plans, policies, procedures and schedules.
  • H: Events describe a happening about which the organisation wishes to keep information as a part of carrying out its mission and conducting its mission.
  • I: Resource object includes and describes any value item, either tangible or intangible, that is owned, managed or used by, or of specific interest to, the organisation in the course of accomplishing its mission.

The key facets of operational awareness detailed above constitute a potential of fundamental importance in the formulation of organizational strategy.

Timely, accurate and appropriate data at this level can temper ambition with the facts on the ground, with operation insight, and with the effectiveness of time and place utilisation.

But take care. In most organisations there will be a spread of attention to the key facets illustrated here, and they will be treated with varying degrees of intensity relative to their overall contribution to strategy formulation. In addition, organizational specific facets may also be introduced where needed in order to complement the overall set of operational awareness facets described here.

[1] IBM’s IFW/BDW.

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