Category Archives: Utilities & Energy

MDM for Utilities: Data Revives a 100 Year-Old Business Model

In both Europe and North America, there’s something profoundly different about today’s Utility Bill. Today’s bill goes well beyond the amount of money you owe for service that month. Today your Utility Bill is full of DATA. Perhaps it contains baseline analytics around usage and temperature, over a twelve month span. Perhaps it contains additional warranties for the power lines that go from the street to your property (typically not covered for repairs by the utility).  In fact, recently, even 3rd party companies have been sending me mail, offering to engage in a fixed-rate payment plan to smooth out my cash flow from the customary projected (from last year) vs actual (read) usage readings. Since smart readers are cropping up everywhere, I was wondering why this “actual vs plan” was still practice, even in the most urban of environments.

MDM for Utilities

Keep flipping-the-switch profitable. Source: thetyee.ca

In fact, a modern utility company has far more intricate data than a consumer sees on a bill. Behind the scenes, utilities leverage a plethora of data pools. Utility companies now have robust asset management, job order and scheduling systems. In addition, they use advanced analytics that monitor sensor data to predict maintenance needs. Mostly importantly, utilities run monthly analytics to prepare rate case requests with local regulators. These are then used to lock in new cost-plus structures for local and business billing in the years ahead.

Unfortunately, most of these applications sit in geographical or departmental silos, and only connect with each other in batch mode, if at all.  These data silos make utilities susceptible to frequent, costly data clean-up projects. These Data clean-up projects always surface the Utility’s shortcomings with regard to data standardization, duplication, linkage and hierarchical structuring. However, until recently, few Utilities were willing to invest to ensure that the newly cleaned data pools remained clean.

Enter MDM for Utilities

Master Data Management is the lynch pin for resolving Utility data issues. Without a clean, enriched, truthful picture of substation, breaker, valve, pump and line information, how can an operator adequately document the need for a rate hike? MDM can help answer questions like:

  • Was that breaker really installed in back in 1900?
  • Or is the year 1900 simply the default date for this data field?
  • Does the substation design mirror what was actually installed?
  • Is the breaker physically located where it is supposed to be?
  • Am I paying maintenance for a breaker that is actually owned by another operator?
  • Why am I sending a crew to inspect equipment that was deemed in-working-order one month earlier?
  • Is the housing development meter really located where the installing contractor claims it was installed?

Without MDM, utilities face all sorts of potential problems:

  1. Maintenance budgets can be either underfunded or overfunded
  2. Job vs bill requests can fail to align with local county delineations
  3. New housing construction can be significantly underbid

MDM for Utilities When a Utility operator uses the wealth of data they possess to optimize their operations, they inevitably reap financial benefits. The Utility company of the future invests in the maintained integrity of their data pool, rather than continually wasting cycles bodies on quarterly data cleansing. To learn how your company can do the same, please register for our Utility Industry MDM webinar on April 1 at 10 AM PST. In the webinar, Informatica and Noah Consulting will address the use cases and financial value MDM can bring to the utility industry.

Disclaimer: Recommendations and illustrations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations and benchmarks.  While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warranty or representation of success, either express or implied, is made.

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Posted in Master Data Management, Utilities & Energy | Tagged , , , , | Leave a comment

Death of the Data Scientist: Silver Screen Fiction?

Maybe the word “death” is a bit strong, so let’s say “demise” instead.  Recently I read an article in the Harvard Business Review around how Big Data and Data Scientists will rule the world of the 21st century corporation and how they have to operate for maximum value.  The thing I found rather disturbing was that it takes a PhD – probably a few of them – in a variety of math areas to give executives the necessary insight to make better decisions ranging from what product to develop next to who to sell it to and where.

Who will walk the next long walk.... (source: Wikipedia)

Who will walk the next long walk…. (source: Wikipedia)

Don’t get me wrong – this is mixed news for any enterprise software firm helping businesses locate, acquire, contextually link, understand and distribute high-quality data.  The existence of such a high-value role validates product development but it also limits adoption.  It is also great news that data has finally gathered the attention it deserves.  But I am starting to ask myself why it always takes individuals with a “one-in-a-million” skill set to add value.  What happened to the democratization  of software?  Why is the design starting point for enterprise software not always similar to B2C applications, like an iPhone app, i.e. simpler is better?  Why is it always such a gradual “Cold War” evolution instead of a near-instant French Revolution?

Why do development environments for Big Data not accommodate limited or existing skills but always accommodate the most complex scenarios?  Well, the answer could be that the first customers will be very large, very complex organizations with super complex problems, which they were unable to solve so far.  If analytical apps have become a self-service proposition for business users, data integration should be as well.  So why does access to a lot of fast moving and diverse data require scarce PIG or Cassandra developers to get the data into an analyzable shape and a PhD to query and interpret patterns?

I realize new technologies start with a foundation and as they spread supply will attempt to catch up to create an equilibrium.  However, this is about a problem, which has existed for decades in many industries, such as the oil & gas, telecommunication, public and retail sector. Whenever I talk to architects and business leaders in these industries, they chuckle at “Big Data” and tell me “yes, we got that – and by the way, we have been dealing with this reality for a long time”.  By now I would have expected that the skill (cost) side of turning data into a meaningful insight would have been driven down more significantly.

Informatica has made a tremendous push in this regard with its “Map Once, Deploy Anywhere” paradigm.  I cannot wait to see what’s next – and I just saw something recently that got me very excited.  Why you ask? Because at some point I would like to have at least a business-super user pummel terabytes of transaction and interaction data into an environment (Hadoop cluster, in memory DB…) and massage it so that his self-created dashboard gets him/her where (s)he needs to go.  This should include concepts like; “where is the data I need for this insight?’, “what is missing and how do I get to that piece in the best way?”, “how do I want it to look to share it?” All that is required should be a semi-experienced knowledge of Excel and PowerPoint to get your hands on advanced Big Data analytics.  Don’t you think?  Do you believe that this role will disappear as quickly as it has surfaced?

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Posted in Big Data, Business Impact / Benefits, CIO, Customer Acquisition & Retention, Customer Services, Data Aggregation, Data Integration, Data Integration Platform, Data Quality, Data Warehousing, Enterprise Data Management, Financial Services, Healthcare, Life Sciences, Manufacturing, Master Data Management, Operational Efficiency, Profiling, Scorecarding, Telecommunications, Transportation, Uncategorized, Utilities & Energy, Vertical | Tagged , , , , | 1 Comment

Sensational Find – $200 Million Hidden in a Teenager’s Bedroom!

That tag line got your attention – did it not?  Last week I talked about how companies are trying to squeeze more value out of their asset data (e.g. equipment of any kind) and the systems that house it.  I also highlighted the fact that IT departments in many companies with physical asset-heavy business models have tried (and often failed) to create a consistent view of asset data in a new ERP or data warehouse application.  These environments are neither equipped to deal with all life cycle aspects of asset information, nor are they fixing the root of the data problem in the sources, i.e. where the stuff is and what it look like. It is like a teenager whose parents have spent thousands of dollars on buying him the latest garments but he always wears the same three outfits because he cannot find the other ones in the pile he hoardes under her bed.  And now they bought him a smart phone to fix it.  So before you buy him the next black designer shirt, maybe it would be good to find out how many of the same designer shirts he already has, what state they are in and where they are.

Finding the asset in your teenager's mess

Finding the asset in your teenager’s mess

Recently, I had the chance to work on a like problem with a large overseas oil & gas company and a North American utility.  Both are by definition asset heavy, very conservative in their business practices, highly regulated, very much dependent on outside market forces such as the oil price and geographically very dispersed; and thus, by default a classic system integration spaghetti dish.

My challenge was to find out where the biggest opportunities were in terms of harnessing data for financial benefit.

The initial sense in oil & gas was that most of the financial opportunity hidden in asset data was in G&G (geophysical & geological) and the least on the retail side (lubricants and gas for sale at operated gas stations).  On the utility side, the go to area for opportunity appeared to be maintenance operations.  Let’s say that I was about right with these assertions but that there were a lot more skeletons in the closet with diamond rings on their fingers than I anticipated.

After talking extensively with a number of department heads in the oil company; starting with the IT folks running half of the 400 G&G applications, the ERP instances (turns out there were 5, not 1) and the data warehouses (3), I queried the people in charge of lubricant and crude plant operations, hydrocarbon trading, finance (tax, insurance, treasury) as well as supply chain, production management, land management and HSE (health, safety, environmental).

The net-net was that the production management people said that there is no issue as they already cleaned up the ERP instance around customer and asset (well) information. The supply chain folks also indicated that they have used another vendor’s MDM application to clean up their vendor data, which funnily enough was not put back into the procurement system responsible for ordering parts.  The data warehouse/BI team was comfortable that they cleaned up any information for supply chain, production and finance reports before dimension and fact tables were populated for any data marts.

All of this was pretty much a series of denial sessions on your 12-step road to recovery as the IT folks had very little interaction with the business to get any sense of how relevant, correct, timely and useful these actions are for the end consumer of the information.  They also had to run and adjust fixes every month or quarter as source systems changed, new legislation dictated adjustments and new executive guidelines were announced.

While every department tried to run semi-automated and monthly clean up jobs with scripts and some off-the-shelve software to fix their particular situation, the corporate (holding) company and any downstream consumers had no consistency to make sensible decisions on where and how to invest without throwing another legion of bodies (by now over 100 FTEs in total) at the same problem.

So at every stage of the data flow from sources to the ERP to the operational BI and lastly the finance BI environment, people repeated the same tasks: profile, understand, move, aggregate, enrich, format and load.

Despite the departmental clean-up efforts, areas like production operations did not know with certainty (even after their clean up) how many well heads and bores they had, where they were downhole and who changed a characteristic as mundane as the well name last and why (governance, location match).

Marketing (Trading) was surprisingly open about their issues.  They could not process incoming, anchored crude shipments into inventory or assess who the counterparty they sold to was owned by and what payment terms were appropriate given the credit or concentration risk associated (reference data, hierarchy mgmt.).  As a consequence, operating cash accuracy was low despite ongoing improvements in the process and thus, incurred opportunity cost.

Operational assets like rig equipment had excess insurance coverage (location, operational data linkage) and fines paid to local governments for incorrectly filing or not renewing work visas was not returned for up to two years incurring opportunity cost (employee reference data).

A big chunk of savings was locked up in unplanned NPT (non-production time) because inconsistent, incorrect well data triggered incorrect maintenance intervals. Similarly, OEM specific DCS (drill control system) component software was lacking a central reference data store, which did not trigger alerts before components failed. If you add on top a lack of linkage of data served by thousands of sensors via well logs and Pi historians and their ever changing roll-up for operations and finance, the resulting chaos is complete.

One approach we employed around NPT improvements was to take the revenue from production figure from their 10k and combine it with the industry benchmark related to number of NPT days per 100 day of production (typically about 30% across avg depth on & offshore types).  Then you overlay it with a benchmark (if they don’t know) how many of these NPT days were due to bad data, not equipment failure or alike, and just fix a portion of that, you are getting big numbers.

When I sat back and looked at all the potential it came to more than $200 million in savings over 5 years and this before any sensor data from rig equipment, like the myriad of siloed applications running within a drill control system, are integrated and leveraged via a Hadoop cluster to influence operational decisions like drill string configuration or asmyth.

Next time I’ll share some insight into the results of my most recent utility engagement but I would love to hear from you what your experience is in these two or other similar industries.

Disclaimer:
Recommendations contained in this post are estimates only and are based entirely upon information provided by the prospective customer  and on our observations.  While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
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Posted in Application Retirement, B2B, Big Data, Business Impact / Benefits, Business/IT Collaboration, CIO, Data Aggregation, Data Governance, Data Integration, Data Quality, Enterprise Data Management, Governance, Risk and Compliance, Manufacturing, Master Data Management, Mergers and Acquisitions, Operational Efficiency, Uncategorized, Utilities & Energy, Vertical | Tagged , , , , , , , | Leave a comment

Squeezing the Value out of the Old Annoying Orange

I believe that most in the software business believe that it is tough enough to calculate and hence financially justify the purchase or build of an application - especially middleware – to a business leader or even a CIO.  Most of business-centric IT initiatives involve improving processes (order, billing, service) and visualization (scorecarding, trending) for end users to be more efficient in engaging accounts.  Some of these have actually migrated to targeting improvements towards customers rather than their logical placeholders like accounts.  Similar strides have been made in the realm of other party-type (vendor, employee) as well as product data.  They also tackle analyzing larger or smaller data sets and providing a visual set of clues on how to interpret historical or predictive trends on orders, bills, usage, clicks, conversions, etc.

Squeeze that Orange

Squeeze that Orange

If you think this is a tough enough proposition in itself, imagine the challenge of quantifying the financial benefit derived from understanding where your “hardware” is physically located, how it is configured, who maintained it, when and how.  Depending on the business model you may even have to figure out who built it or owns it.  All of this has bottom-line effects on how, who and when expenses are paid and revenues get realized and recognized.  And then there is the added complication that these dimensions of hardware are often fairly dynamic as they can also change ownership and/or physical location and hence, tax treatment, insurance risk, etc.

Such hardware could be a pump, a valve, a compressor, a substation, a cell tower, a truck or components within these assets.  Over time, with new technologies and acquisitions coming about, the systems that plan for, install and maintain these assets become very departmentalized in terms of scope and specialized in terms of function.  The same application that designs an asset for department A or region B, is not the same as the one accounting for its value, which is not the same as the one reading its operational status, which is not the one scheduling maintenance, which is not the same as the one billing for any repairs or replacement.  The same folks who said the Data Warehouse is the “Golden Copy” now say the “new ERP system” is the new central source for everything.  Practitioners know that this is either naiveté or maliciousness. And then there are manual adjustments….

Moreover, to truly take squeeze value out of these assets being installed and upgraded, the massive amounts of data they generate in a myriad of formats and intervals need to be understood, moved, formatted, fixed, interpreted at the right time and stored for future use in a cost-sensitive, easy-to-access and contextual meaningful way.

I wish I could tell you one application does it all but the unsurprising reality is that it takes a concoction of multiple.  None or very few asset life cycle-supporting legacy applications will be retired as they often house data in formats commensurate with the age of the assets they were built for.  It makes little financial sense to shut down these systems in a big bang approach but rather migrate region after region and process after process to the new system.  After all, some of the assets have been in service for 50 or more years and the institutional knowledge tied to them is becoming nearly as old.  Also, it is probably easier to engage in often required manual data fixes (hopefully only outliers) bit-by-bit, especially to accommodate imminent audits.

So what do you do in the meantime until all the relevant data is in a single system to get an enterprise-level way to fix your asset tower of Babel and leverage the data volume rather than treat it like an unwanted step child?  Most companies, which operate in asset, fixed-cost heavy business models do not want to create a disruption but a steady tuning effect (squeezing the data orange), something rather unsexy in this internet day and age.  This is especially true in “older” industries where data is still considered a necessary evil, not an opportunity ready to exploit.  Fact is though; that in order to improve the bottom line, we better get going, even if it is with baby steps.

If you are aware of business models and their difficulties to leverage data, write to me.  If you even know about an annoying, peculiar or esoteric data “domain”, which does not lend itself to be easily leveraged, share your thoughts.  Next time, I will share some examples on how certain industries try to work in this environment, what they envision and how they go about getting there.

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Posted in Application Retirement, Big Data, Business Impact / Benefits, Business/IT Collaboration, CIO, Customer Acquisition & Retention, Customers, Data Governance, Data Quality, Enterprise Data Management, Governance, Risk and Compliance, Healthcare, Life Sciences, Manufacturing, Master Data Management, Mergers and Acquisitions, Operational Efficiency, Product Information Management, Profiling, Telecommunications, Transportation, Utilities & Energy, Vertical | 1 Comment