Data Structuring/ Normalization and Validation the Hidden Key to Unified Information Solution!!!

For the last 2 weeks I have been on the road, sorry just did not get to updating blog, but a constant discussion topic that keeps coming up. “How to truly build a Unified Information system that allows a view across their industrial/ production assets, and the ability to compare, and understand current state, capacity and performance quickly and easily.”

People have tried the intelligence, information semantic products which layer on top and bring valuable representation of information, but while the semantic information is a critical role, and aligns data silos, it is only as good as the data.

When discussing strategies with companies in Upstream oil and gas, Mining, Metals, Refining, Power, Water Utilities, and a common theme is the need to get data structuring/ normalization and key is validation of data.

One person commented last week “we cannot just take all the data from our existing SCADA systems/ historians and bring it to central historian due to no alignment, lack of consistent context, and data validation!”

The problem is not just about aligning data for access, the goal today is to make decisions not just about one process or plant but timely decisions across multiple sites. To achieve this it is not just about gathering existing data, as one set of engineers put it, we need to normalized data this means units and measures been aligned for one. They are putting unifying storage systems but they spend a lot of time going to the source of actual data, and then structuring and normalizing measures. Today this is being done in the control layer and supervisory layer. It is also key that the existing running systems on a plant are not disrupted, so the risk to changing these running systems in these systems is way too high. 

In one case a customer talked about 16 versions of one measure across 14 plants. This same story comes all the time, so the questions is how to achieve a result that does not threaten the existing sites, especially when the challenge today is not in a plant but across sites. These sites have been put in at different times with different systems, and often different objectives.

The key is to look at a layer that allows:

·         cross site administration/ configuration e.g. a distributed system

·         ability establish standards for asset structures / kpis / measures/ units, with the ability to centrally administer these standards and enforce governance

·         ability data collect from both real time and historical data stores

·         ability to set up in these structures validation rules that will validate the captured data to trusted before storage

·         distributed data capture and ability to store locally and aggregate providing data integrity.

The key is a 3 layer solution:

• A data collection platform that can be phyically be distributed but centrally managed with standards governance, providing data structure, intellignece, and validation as close to the source as possible. So that different process/ plant data both realtime and historiacl can be aligned across this common namespace.
• A storage layer that the platform now propagates this now normalized information into one or multiple distributed storage historians, providing a consistent information storage across these different control/ automation systems.
• A unifying information layer with a semantic capability to allow this unified storage + the operational storages from different systems to be aligned. 

These three layers enable success as a team, not any one of them, if you are looking for unified information system that can be trusted, so decisions in the now can be made.

How Pre Context/ Pre Normalizing vs Traditional Post Context DATA/ INFORMATION is critical to Operational Empowerment

Over the last couple weeks I have ranted on about what is happening that makes this Operational Empowerment Revolution not a nice to have but a necessary in today’s changing/ rotating workforce.  Then I sit in a room and listen to people about “just put a historian in and capture the data we will use it latter to tune the plant”. Yet in the next breadth the comment and vision of operational execution/ excellence with procedures and MES like execution is discussed. I feel cold in the seat, as the vision is correct, the passion is there, but the immediate execution will put them down a path to constrain their ability to go to vision. 

So it was time to pipe up, and get up on the white board and enjoy myself in concepts and opportunity. The data centric model is not bad it works but it brings up shortfalls in ability to evolve to more proactive. I believe as the market moves more and more into all decisions in the NOW, traditional approaches to information systems will be good for analysis, tuning, and post actions, but that is not NOW.

As the worker no matter their role evolves every day to an increasingly Knowledge worker, performing actions and responsibilities beyond their traditional roles, the expectation is a more adaptive business. In the example above the CIO was describing a modern operational Now vision, yet applying a tradition post action solution. We have always heard the next step beyond intelligence and information is action, and this is so true, but the step beyond that is pre-emptive information/ condition and action in the NOW time frame.

The requirements for a information system today is seen in the diagram the key points are:

·   You capture the data as close to source as possible for data integrity

·   You validate (more that just bad data) but is the reading actually valid for the state of the process.

·    The data is then put in context in a “Living agent/ object etc” that can bring in validate, but also do calculation/ kpis etc and generate events. The data is effectively transformed into information that can be stored and triggered into immediate action, but initiating an alarm, or workflow.

·    The storage layer is the tradition historian which could be distributed or central, and could support Tiered  but in a one name space created the layer below in the plat model, providing unique names and context.

·   Operational extensions these are a series of programs/ modules which should extend the plant model with more information, valued context.

·    Then the unifying information model that is accessed and interacted with through rich set of content and analysis clients.

This is vs the traditional Historian approach that captures data un validated and structured and does post normalization.

The pre approach seems more accommodating to evolution, value and ability to take actions in the now, it is also less risk, using a combination of System Platform with the historian built in.

It is time to rethink based upon the “job to Be Done “ today, and the context of the job, the technologies are available!