Category Archives: Operational Efficiency
“Not only do we underestimate the cost for projects up to 150%, but we overestimate the revenue it will generate.” This quotation from an Energy & Petroleum (E&P) company executive illustrates the negative impact of inaccurate, inconsistent and disconnected well data and asset data on revenue potential.
“Operational Excellence” is a common goal of many E&P company executives pursuing higher growth targets. But, inaccurate, inconsistent and disconnected well data and asset data may be holding them back. It obscures the complete picture of the well information lifecycle, making it difficult to maximize production efficiency, reduce Non-Productive Time (NPT), streamline the oilfield supply chain, calculate well by-well profitability, and mitigate risk.
To explain how E&P companies can better manage well data and asset data, we hosted a webinar, “Attention E&P Executives: Streamlining the Well Information Lifecycle.” Our well data experts Stephanie Wilkin, Senior Principal Consultant at Noah Consulting, and Stephan Zoder, Director of Value Engineering at Informatica shared some advice. E&P companies should reevaluate “throwing more bodies at a data cleanup project twice a year.” This approach does not support the pursuit of operational excellence.
In this interview, Stephanie shares details about the award-winning collaboration between Noah Consulting and Devon Energy to create a single trusted source of well data, which is standardized and mastered.
Q. Congratulations on winning the 2014 Innovation Award, Stephanie!
A. Thanks Jakki. It was really exciting working with Devon Energy. Together we put the technology and processes in place to manage and master well data in a central location and share it with downstream systems on an ongoing basis. We were proud to win the 2014 Innovation Award for Best Enterprise Data Platform.
Q. What was the business need for mastering well data?
A. As E&P companies grow so do their needs for business-critical well data. All departments need clean, consistent and connected well data to fuel their applications. We implemented a master data management (MDM) solution for well data with the goals of improving information management, business productivity, organizational efficiency, and reporting.
Q. How long did it take to implement the MDM solution for well data?
A. The Devon Energy project kicked off in May of 2012. Within five months we built the complete solution from gathering business requirements to development and testing.
Q. What were the steps in implementing the MDM solution?
A: The first and most important step was securing buy-in on a common definition for master well data or Unique Well Identifier (UWI). The key was to create a definition that would meet the needs of various business functions. Then we built the well master, which would be consistent across various systems, such as G&G, Drilling, Production, Finance, etc. We used the Professional Petroleum Data Management Association (PPDM) data model and created more than 70 unique attributes for the well, including Lahee Class, Fluid Direction, Trajectory, Role and Business Interest.
As part of the original go-live, we had three source systems of well data and two target systems connected to the MDM solution. Over the course of the next year, we added three additional source systems and four additional target systems. We did a cross-system analysis to make sure every department has the right wells and the right data about those wells. Now the company uses MDM as the single trusted source of well data, which is standardized and mastered, to do analysis and build reports.
Q. What’s been the traditional approach for managing well data?
A. Typically when a new well is created, employees spend time entering well data into their own systems. For example, one person enters well data into the G&G application. Another person enters the same well data into the Drilling application. A third person enters the same well data into the Finance application. According to statistics, it takes about 30 minutes to enter wells into a particular financial application.
So imagine if you need to add 500 new wells to your systems. This is common after a merger or acquisition. That translates to roughly 250 hours or 6.25 weeks of employee time saved on the well create process! By automating across systems, you not only save time, you eliminate redundant data entry and possible errors in the process.
Q. That sounds like a painfully slow and error-prone process.
A. It is! But that’s only half the problem. Without a single trusted source of well data, how do you get a complete picture of your wells? When you compare the well data in the G&G system to the well data in the Drilling or Finance systems, it’s typically inconsistent and difficult to reconcile. This leads to the question, “Which one of these systems has the best version of the truth?” Employees spend too much time manually reconciling well data for reporting and decision-making.
Q. So there is a lot to be gained by better managing well data.
A. That’s right. The CFO typically loves the ROI on a master well data project. It’s a huge opportunity to save time and money, boost productivity and get more accurate reporting.
Q: What were some of the business requirements for the MDM solution?
A: We couldn’t build a solution that was narrowly focused on meeting the company’s needs today. We had to keep the future in mind. Our goal was to build a framework that was scalable and supportable as the company’s business environment changed. This allows the company to add additional data domains or attributes to the well data model at any time.
Q: Why did you choose Informatica MDM?
A: The decision to use Informatica MDM for the MDM Trust Framework came down to the following capabilities:
- Match and Merge: With Informatica, we get a lot of flexibility. Some systems carry the API or well government ID, but some don’t. We can match and merge records differently based on the system.
- X-References: We keep a cross-reference between all the systems. We can go back to the master well data and find out where that data came from and when. We can see where changes have occurred because Informatica MDM tracks the history and lineage.
- Scalability: This was a key requirement. While we went live after only 5 months, we’ve been continually building out the well master based on the requiremets of the target systems.
- Flexibility: Down the road, if we want to add an additional facet or classification to the well master, the framework allows for that.
- Simple Integration: Instead of building point-to-point integrations, we use the hub model.
In addition to Informatica MDM, our Noah Consulting MDM Trust Framework includes Informatica PowerCenter for data integration, Informatica Data Quality for data cleansing and Informatica Data Virtualization.
Q: Can you give some examples of the business value gained by mastering well data?
A: One person said to me, “I’m so overwhelmed! We’ve never had one place to look at this well data before.” With MDM centrally managing master well data and fueling key business applications, many upstream processes can be optimized to achieve their full potential value.
People spend less time entering well data on the front end and reconciling well data on the back end. Well data is entered once and it’s automatically shared across all systems that need it. People can trust that it’s consistent across systems. Also, because the data across systems is now tied together, it provides business value they were unable to realize before, such as predictive analytics.
Q. What’s next?
A. There’s a lot of insight that can be gained by understanding the relationships between the well, and the people, equipment and facilities associated with it. Next, we’re planning to add the operational hierarchy. For example, we’ll be able to identify which production engineer, reservoir engineer and foreman are working on a particular well.
We’ve also started gathering business requirements for equipment and facilities to be tied to each well. There’s a lot more business value on the horizon as the company streamlines their well information lifecycle and the valuable relationships around the well.
If you missed the webinar, you can watch the replay now: Attention E&P Executives: Streamlining the Well Information Lifecycle.
I have been in marketing for over two decades. As I meet people in social situations, on airplanes, and on the sidelines at children’s soccer games, and they ask what it is I do, I get responses that constantly amuse me and lead me to the conclusion that the general public has absolutely no idea what a marketer does. I am often asked things like “have you created any commercials that I might have seen?” and peppered with questions that evoke visions of Mad Men-esque 1960’s style agency work and late night creative martini-filled pitch sessions.
I admit I do love to catch the occasional Mad Men episode, and a few weeks ago, I stumbled upon one that had me chuckling. You may remember the one that Don Draper is pitching a lipstick advertisement and after persuading the executive to see things his way, he says something along the lines of, “We’ll never know, will we? It’s not a science.”
How the times have changed. I would argue that in today’s data-driven world, marketing is no longer an art and is now squarely a science.
Sure, great marketers still understand their buyers at a gut level, but their hunches are no longer the impetus of a marketing campaign. Their hunches are now the impetus for a data-driven, fact-finding mission, and only after the analysis has been completed and confirms or contradicts this hunch, is the campaign designed and launched.
This is only possible because today, marketers have access to enormous amounts of data – not just the basic demographics of years past. Most marketers realize that there is great promise in all of that data, but it’s just too complicated, time-consuming, and costly to truly harness it. How can you really ever make sense of the hundreds of data sources and tens of thousands of variables within these sources? Social media, web analytics, geo-targeting, internal customer and financial systems, in house marketing automation systems, third party data augmentation in the cloud… the list goes on and on!
How can marketers harness the right data, in the right way, right away? The answer starts with making the commitment that your marketing team – and hopefully your organization as a whole – will think “data first”. In the coming weeks, I will focus on what exactly thinking data first means, and how it will pay dividends to marketers.
In the mean time, I will make the personal commitment to be more patient about answering the silly questions and comments about marketers.
Now, it’s your turn to comment…
What are some of the most amusing misconceptions about marketers that you’ve encountered?
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Do you agree? Is marketing an art? A science? Or somewhere in between?
Before I joined Informatica I worked for a health plan in Boston. I managed several programs including CMS Five Start Quality Rating System and Risk Adjustment Redesign. We recognized the need for a robust diagnostic profile of our members in support of risk adjustment. However, because the information resides in multiple sources, gathering and connecting the data presented many challenges. I see the opportunity for health plans to transform risk adjustment.
As risk adjustment becomes an integral component in healthcare, I encourage health plans to create a core competency around the development of diagnostic profiles. This should be the case for health plans and ACO’s. This profile is the source of reimbursement for an individual. This profile is also the basis for clinical care management. Augmented with social and demographic data, the profile can create a roadmap for successfully engaging each member.
Why is risk adjustment important?
Risk Adjustment is increasingly entrenched in the healthcare ecosystem. Originating in Medicare Advantage, it is now applicable to other areas. Risk adjustment is mission critical to protect financial viability and identify a clinical baseline for members.
What are a few examples of the increasing importance of risk adjustment?
1) Centers for Medicare and Medicaid (CMS) continues to increase the focus on Risk Adjustment. They are evaluating the value provided to the Federal government and beneficiaries. CMS has questioned the efficacy of home assessments and challenged health plans to provide a value statement beyond the harvesting of diagnoses codes which result solely in revenue enhancement. Illustrating additional value has been a challenge. Integrating data across the health plan will help address this challenge and derive value.
2) Marketplace members will also require risk adjustment calculations. After the first three years, the three “R’s” will dwindle down to one ‘R”. When Reinsurance and Risk Corridors end, we will be left with Risk Adjustment. To succeed with this new population, health plans need a clear strategy to obtain, analyze and process data. CMS processing delays make risk adjustment even more difficult. A Health Plan’s ability to manage this information will be critical to success.
3) Dual Eligibles, Medicaid members and ACO’s also rely on risk management for profitability and improved quality.
With an enhanced diagnostic profile — one that is accurate, complete and shared — I believe it is possible to enhance care, deliver appropriate reimbursements and provide coordinated care.
How can payers better enable risk adjustment?
- Facilitate timely analysis of accurate data from a variety of sources, in any format.
- Integrate and reconcile data from initial receipt through adjudication and submission.
- Deliver clean and normalized data to business users.
- Provide an aggregated view of master data about members, providers and the relationships between them to reveal insights and enable a differentiated level of service.
- Apply natural language processing to capture insights otherwise trapped in text based notes.
With clean, safe and connected data, health plans can profile members and identify undocumented diagnoses. With this data, health plans will also be able to create reports identifying providers who would benefit from additional training and support (about coding accuracy and completeness).
What will clean, safe and connected data allow?
- Allow risk adjustment to become a core competency and source of differentiation. Revenue impacts are expanding to lines of business representing larger and increasingly complex populations.
- Educate, motivate and engage providers with accurate reporting. Obtaining and acting on diagnostic data is best done when the member/patient is meeting with the caregiver. Clear and trusted feedback to physicians will contribute to a strong partnership.
- Improve patient care, reduce medical cost, increase quality ratings and engage members.
What springs to mind when you think about old applications? What happens to them when they outlived their usefulness? Do they finally get to retire and have their day in the sun, or do they tenaciously hang on to life?
Think for a moment about your situation and of those around you. From the time work started you have been encouraged and sometimes forced to think about, plan for and fund your own retirement. Now consider the portfolio your organization has built up over the years; hundreds or maybe thousands of apps, spread across numerous platforms and locations – A mix of home-grown with the best-in-breed tools or acquired from the leading application vendors.
Evaluating Your Current Situation
- Do you know how many of those “legacy” systems are still running?
- Do you know how much these apps are costing?
- Is there a plan to retire them?
- How is the execution tracking to plan?
Truth is, even if you have a plan, it probably isn’t going well.
Providing better citizen service at a lower cost
This is something every state and local organization aspires to do by reducing costs. Many organizations are spending 75% or more of their budgets on just keeping the lights on – maintaining existing applications and infrastructure. Being able to fully retire some, or many of these applications saves significant money. Do you know how much these applications are costing your organization? Don’t forget to include the whole range of costs that applications incur – including the physical infrastructure costs such as mainframes, networks and storage, as well as the required software licenses and of course the time of the people that actually keep them running. What happens when those with with Cobol and CICS experience retire? Usually the answer is not good news. There is a lot to consider and many benefits to be gained through an effective application retirement strategy.
August 2011 report by ESG Global shows that some 68% of organizations had over six or more legacy applications running and that 50% planned to retire at least one of those over the following 12-18 months. It would be interesting to see today’s situation and be able evaluate how successful these application retirement plans have been.
A common problem is knowing where to start. You know there are applications that you should be able to retire, but planning, building and executing an effective and success plan can be tough. To help this process we have developed a strategy, framework and solution for effective and efficient application retirement. This is a good starting point on your application retirement journey.
To get a speedy overview, take six minutes to watch this video on application retirement.
We have created a community specifically for application managers in our ‘Potential At Work’ site. If you haven’t already signed up, take a moment and join this group of like-minded individuals from across the globe.
Recently, I presented a Business Value Assessment to a client. The findings were based on a revenue-generating state government agency. Everyone at the presentation was stunned to find out how much money was left on the table by not basing their activities on transactions, which could be cleanly tied to the participating citizenry and a variety of channel partners. There was over $38 million in annual benefits left over, which included partially recovered lost revenue, cost avoidance and reduction. A higher data impact to this revenue driven business model could have prevented this.
Given the total revenue volume, this may seem small. However, after factoring in the little technology effort required to “collect and connect” data from existing transactions, it is actually extremely high.
The real challenge for this organization will be the required policy transformation to turn the organization from “data-starved” to “data-intensive”. This would eliminate strategic decisions around new products, locations and customers relying on surveys that face sampling errors, biases, etc. Additionally, surveys are often delayed, making them practically ineffective in this real-time world we live in today.
Despite no applicable legal restrictions, the leadership’s main concern was that gathering more data would erode the public’s trust and positive image of the organization.
To be clear; by “more” data being collected by this type of government agency I mean literally 10% of what any commercial retail entity has gathered on all of us for decades. This is not the next NSA revelation as any conspiracy theorist may fear.
While I respect their culturally driven self-censorship despite no legal barricades, it raises their stakeholders’ (the state’s citizenry) concern over its performance. To be clear, there would be no additional revenue for the state’s programs without more citizen data. You may believe that they already know everything about you, including your income, property value, tax information, etc. However, inter-departmental sharing of criminally-non-relevant information is legally constrained.
Another interesting finding from this evaluation was that they had no sense of conversion rate from email and social media campaigns. Impressions from click-throughs as well as hard/soft bounces were more important than tracking who actually generated revenue.
This is a very market-driven organization compared to other agencies. It actually does try to measure itself like a commercial enterprise and attempts to change in order to generate additional revenue for state programs benefiting the citizenry. I can only imagine what non-revenue-generating agencies (local, state or federal) do in this respect. Is revenue-oriented thinking something the DoD, DoJ or Social Security should subscribe to?
Think tanks and political pundits are now looking at the trade-off between bringing democracy to every backyard on our globe and its long-term, budget ramifications. The DoD is looking to reduce the active component to its lowest in decades given the U.S. federal debt level.
A recent article in HBR explains that cost cutting has never sustained an organization’s growth over a longer period of time, but new revenue sources did. Is your company or government agency only looking at cost and personnel productivity?
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.
Looking for a data integration expert? Join the club. As cloud computing and big data become more desirable within the Global 2000, an abundance of data integration talent is required to make both cloud and big data work properly.
The fact of the matter is that you can’t deploy a cloud-based system without some sort of data integration as part of the solution. Either from on-premise to cloud, cloud-to-cloud, or even intra-company use of private clouds, these projects need someone who knows what they are doing when it comes to data integration.
While many cloud projects were launched without a clear understanding of the role of data integration, most people understand it now. As companies become more familiar with the could, they learn that data integration is key to the solution. For this reason, it’s important for teams to have at least some data integration talent.
The same goes for big data projects. Massive amounts of data need to be loaded into massive databases. You can’t do these projects using ad-hoc technologies anymore. The team needs someone with integration knowledge, including what technologies to bring to the project.
Generally speaking, big data systems are built around data integration solutions. Similar to cloud, the use of data integration architectural expertise should be a core part of the project. I see big data projects succeed and fail, and the biggest cause of failure is the lack of data integration expertise.
The demand for data integration talent has exploded with the growth of both big data and cloud computing. A week does not go by that I’m not asked for the names of people who have data integration, cloud computing and big data systems skills. I know several people who fit that bill, however they all have jobs and recently got raises.
The scary thing is, if these jobs go unfilled by qualified personnel, project directors may hire individuals without the proper skills and experience. Or worse, they may not hire anyone at all. If they plod along without the expertise required, in a year they’ll wonder why the systems are not sharing data the way they should, resulting in a big failure.
So, what can organizations do? You can find or build the talent you need before starting important projects. Thus, now is the time to begin the planning process, including how to find and hire the right resources. This might even mean internal training, hiring mentors or outside consultants, or working with data integration technology providers. Do everything necessary to make sure you get data integration done right the first time.
Analyzing current business trends helps illustrate how difficult and complex the Communication Service Provider business environment has become. CSPs face many challenges. Clients expect high quality, affordable content that can move between devices with minimum advertising or privacy concerns. To illustrate this phenomenon, here are a few recent examples:
- Apple is working with Comcast/NBC Universal on a new converged offering
- Vodafone purchased the Spanish cable operator, Ono, having to quickly separate the wireless customers from the cable ones and cross-sell existing products
- Net neutrality has been scuttled in the US and upheld in the EU so now a US CSP can give preferential bandwidth to content providers, generating higher margins
- Microsoft’s Xbox community collects terabytes of data every day making effective use, storage and disposal based on local data retention regulation a challenge
- Expensive 4G LTE infrastructure investment by operators such as Reliance is bringing streaming content to tens of millions of new consumers
To quickly capitalize on “new” (often old, but unknown) data sources, there has to be a common understanding of:
- Where the data is
- What state it is in
- What it means
- What volume and attributes are required to accommodate a one-off project vs. a recurring one
When a multitude of departments request data for analytical projects with their one-off, IT-unsanctioned on-premise or cloud applications, how will you go about it? The average European operator has between 400 and 1,500 (known) applications. Imagine what the unknown count is.
A European operator with 20-30 million subscribers incurs an average of $3 million per month due to unpaid invoices. This often results from incorrect or incomplete contact information. Imagine how much you would have to add for lost productivity efforts, including gathering, re-formatting, enriching, checking and sending invoices. And this does not even account for late invoice payments or extended incorrect credit terms.
Think about all the wrong long-term conclusions that are being drawn from this wrong data. This single data problem creates indirect cost in excess of three times the initial, direct impact of unpaid invoices.
Want to fix your data and overcome the accelerating cost of change? Involve your marketing, CEM, strategy, finance and sales leaders to help them understand data’s impact on the bottom line.
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.
Over the past few years, we have assisted an increasing shift in customer behavior. Pervasive internet connectivity – along with the exponential adoption of mobile devices – has enabled shoppers to research and purchase products of all kinds, anytime and anywhere, using a combination of touch points they find most convenient. This is not a passing fad.
Consumers expect rich data and images to make purchase choices; business users require access to analytical data in order to make mission-critical decisions. These demands for information are driving a need for improved product data availability and accuracy. And this is changing the way businesses go to market.
A staggering number of stores and manufacturers are reforming their models to response to this challenge. The direct-to-consumer (DTC) model, while not new, is rapidly becoming the center stage to address these challenges. The optimal DTC model will vary depending on specific and contextual business objectives. However, there are many strategic benefits to going direct, but the main objectives include growing sales, gaining control over pricing, strengthening the brand, getting closer to consumers, and testing out new products and markets.
It is my contention that while the DTC model is gaining the deserved attention, much remains to be done. In fact, among many challenges that DTC poses, the processes and activities associated with sourcing product information, enriching product data to drive sales and lower returns, and managing product assortments across all channels loom large. More precisely, the challenges that need to be overcome are better exemplified by these points:
- Products have several variations to support different segments, markets, and campaigns.
- Product components, ingredients, care information, environmental impact data and other facets of importance to the customer.
- People are visual. As a result, easy website navigation is essential. Eye-catching images that highlight your products or services (perhaps as they’re being performed or displayed as intended) is an effective way to visually communicate information to your customers and make it easier for them to evaluate options. If information and pictures are readily accessible, customers are more likely to engage.
- Ratings, reviews and social data, stored within the product’s record rather than in separate systems.
- Purchasing and sales measurements, for example, sales in-store, return rates, sales velocity, product views online, as well as viewing and purchasing correlations are often held across several systems. However, this information is increasingly needed for search and recommendation.
The importance of product data and its use, combined with the increased demands on business as a result of inefficient, non-scaling approaches to data management, provide an imperative to considering a PIM to ‘power’ cross-channel retail. Once established, PIM users repeatedly report higher ROI. It is likely that we’ll see PIM systems rank alongside CRM, ERP, CMS, order management and merchandising systems as the pillars of cross-channel retailing at scale.
For all these reasons, choosing the right PIM strategy (and partner) is now a key decision. Get this decision wrong and it could become an expensive mistake.
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.
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?
In a previous blog post, I wrote about when business “history” is reported via Business Intelligence (BI) systems, it’s usually too late to make a real difference. In this post, I’m going to talk about how business history becomes much more useful when combined operationally and in real time.
E. P. Thompson, a historian pointed out that all history is the history of unintended consequences. His idea / theory was that history is not always recorded in documents, but instead is ultimately derived from examining cultural meanings as well as the structures of society through hermeneutics (interpretation of texts) semiotics and in many forms and signs of the times, and concludes that history is created by people’s subjectivity and therefore is ultimately represented as they REALLY live.
The same can be extrapolated for businesses. However, the BI systems of today only capture a miniscule piece of the larger pie of knowledge representation that may be gained from things like meetings, videos, sales calls, anecdotal win / loss reports, shadow IT projects, 10Ks and Qs, even company blog posts – the point is; how can you better capture the essence of meaning and perhaps importance out of the everyday non-database events taking place in your company and its activities – in other words, how it REALLY operates.
One of the keys to figuring out how businesses really operate is identifying and utilizing those undocumented RULES that are usually underlying every business. Select company employees, often veterans, know these rules intuitively. If you watch them, and every company has them, they just have a knack for getting projects pushed through the system, or making customers happy, or diagnosing a problem in a short time and with little fanfare. They just know how things work and what needs to be done.
These rules have been, and still are difficult to quantify and apply or “Data-ify” if you will. Certain companies (and hopefully Informatica) will end up being major players in the race to datify these non-traditional rules and events, in addition to helping companies make sense out of big data in a whole new way. But in daydreaming about it, it’s not hard to imagine business systems that will eventually be able to understand the optimization rules of a business, accounting for possible unintended scenarios or consequences, and then apply them in the time when they are most needed. Anyhow, that’s the goal of a new generation of Operational Intelligence systems.
In my final post on the subject, I’ll explain how it works and business problems it solves (in a nutshell). And if I’ve managed to pique your curiosity and you want to hear about Operational Intelligence sooner, tune in to to a webinar we’re having TODAY at 10 AM PST. Here’s the link.