Tag Archives: Data Management
I recently read an opinion piece written in an insurance publication online. The author postulated, among other things, that the Internet of Things would magically deliver great data to an insurer. Yes, it was a statement just that glib. Almost as if there is some fantastic device that you just plug into the wall and out streams a flow of unicorns and rainbows. And furthermore that those unicorns and rainbows will subsequently give a magical boost to your business. But hey, you plugged in that fantastic device, so bring on the magic.
Now, let’s come back from the land of fairytales and ground ourselves in reality. Data is important, no doubt about that. Today, financial services firms are able to access data from so many new data sources. One of those new and fancy data sources is the myriad of devices in this thing we call the Internet of Things.
You ever have one of those frustrating days with your smart phone? Dropped calls, slow Internet, Facebook won’t locate you? Well, other devices experience the same wonkiness. Even the most robust of devices found on commercial aircraft or military equipment are not lossless in data transmission. And that’s where we are with the Internet of Things. All great devices, they serve a number of purposes, but are still fallible in communicating with the “mother ship”.
A telematics device in a consumer vehicle can transmit, VIN, speed, latitude/longitude, time, and other vehicle statuses for use in auto insurance. As with other devices on a network, some of these data elements will not come through reliably. That means that in order to reconstruct or smooth the set of data, interpolations need to be made and/or entire entries deleted as useless. That is the first issue. Second, simply receiving this isolated dataset does not make sense of it. The data needs to be moved, cleansed and then correlated to other pieces of the puzzle, which eventually turn into a policyholder, an account holder, a client or a risk. And finally, that enhanced data can be used for further analytics. It can be archived, aggregated, warehoused and secured for additional analysis. None of these activities happen magically. And the sheer volume of integration points and data requires a robust and standardized data management infrastructure.
So no, just having an open channel to the stream of noise from your local Internet of Things will not magically deliver you great data. Great data comes from market leading data management solutions from Informatica. So whether you are an insurance company, financial services firm or data provider, being “Insurance Ready” means having great data; ready to use; everywhere…from Informatica.
In last 50-60 years, we have witnessed another revolution, through the invention of computing machines and the Internet – a digital revolution. It has transformed every industry and allowed us to operate at far greater scale – processing more transactions and in more locations – than ever before. New cities emerged on the map, migrations of knowledge workers throughout the world followed, and the standard of living increased again. And digitally available information transformed how we run businesses, cities, or countries.
Forces Shaping Digital Revolution
Over the last 5-6 years, we’ve witnessed a massive increase in the volume and variety of this information. Leading forces that contributed to this increase are:
- Next generation of software technology connecting data faster from any source
- Little to no hardware cost to process and store huge amount of data (Moore’s Law)
- A sharp increase in number of machines and devices generating data that are connected online
- Massive worldwide growth of people connecting online and sharing information
- Speed of Internet connectivity that’s now free in many public places
As a result, our engagement with the digital world is rising – both for personal and business purposes. Increasingly, we play games, shop, sign digital contracts, make product recommendations, respond to customer complains, share patient data, and make real time pricing changes to in-store products – all from a mobile device or laptop. We do so increasingly in a collaborative way, in real-time, and in a very personalized fashion. Big Data, Social, Cloud, and Internet of Things are key topics dominating our conversations and thoughts around data these days. They are altering our ways to engage with and expectations from each other.
This is the emergence of a new revolution or it is the next phase of our digital revolution – the democratization and ubiquity of information to create new ways of interacting with customers and dramatically speeding up market launch. Businesses will build new products and services and create new business models by exploiting this vast new resource of information.
The Quest for Great Data
But, there is work to do before one can unleash the true potential captured in data. Data is no more a by-product or transaction record. Neither it has anymore an expiration date. Data now flows through like a river fueling applications, business processes, and human or machine activities. New data gets created on the way and augments our understanding of the meaning behind this data. It is no longer good enough to have good data in isolated projects, but rather great data need to become accessible to everyone and everything at a moment’s notice. This rich set of data needs to connect efficiently to information that has been already present and learn from it. Such data need to automatically rid itself of inaccurate and incomplete information. Clean, safe, and connected – this data is now ready to find us even before we discover it. It understands the context in which we are going to make use of this information and key decisions that will follow. In the process, this data is learning about our usage, preference, and results. What works versus what doesn’t. New data is now created that captures such inherent understanding or intelligence. It needs to flow back to appropriate business applications or machines for future usage after fine-tuning. Such data can then tell a story about human or machine actions and results. Such data can become a coach, a mentor, a friend of kind to guide us through critical decision points. Such data is what we would like to call great data. In order to truly capitalize on the next step of digital revolution, we will pervasively need this great data to power our decisions and thinking.
Impacting Every Industry
By 2020, there’ll be 50 Billion connected devices, 7x more than human beings on the planet. With this explosion of devices and associated really big data that will be processed and stored increasingly in the cloud. More than size, this complexity will require a new way of addressing business process efficiency that renders agility, simplicity, and capacity. Impact of such transformation will spread across many industries. A McKinsey article, “The Future of Global Payments”, focuses on digital transformation of payment systems in the banking industry and ubiquity of data as a result. One of the key challenges for banks will be to shift from their traditional heavy reliance on siloed and proprietary data to a more open approach that encompasses a broader view of customers.
Industry executives, front line managers, and back office workers are all struggling to make the most sense of the data that’s available.
Closing Thoughts on Great Data
A “2014 PWC Global CEO Survey ” showed 81% ranked technology advances as #1 factor to transform their businesses over next 5 years. More data, by itself, isn’t enough for this transformation. A robust data management approach integrating machine and human data, from all sources and updated in real-time, among on-premise and cloud-based systems must be put in place to accomplish this mission. Such an approach will nurture great data. This end-to-end data management platform will provide data guidance and curate an organization’s one of the most valuable assets, its information. Only by making sense of what we have at our disposal, will we unleash the true potential of the information that we possess. The next step in the digital revolution will be about organizations of all sizes being fueled by great data to unleash their potential tapped.
“Raw materials costs are the company’s single largest expense category,” said Steve Jenkins, Global IT Director at Valspar, at MDM Day in London. “Data management technology can help us improve business process efficiency, manage sourcing risk and reduce RFQ cycle times.”
Valspar is a $4 billion global manufacturing company, which produces a portfolio of leading paint and coating brands. At the end of 2013, the 200 year old company celebrated record sales and earnings. They also completed two acquisitions. Valspar now has 10,000 employees operating in 25 countries.
As is the case for many global companies, growth creates complexity. “Valspar has multiple business units with varying purchasing practices. We source raw materials from 1,000s of vendors around the globe,” shared Steve.
“We want to achieve economies of scale in purchasing to control spending,” Steve said as he shared Valspar’s improvement objectives. “We want to build stronger relationships with our preferred vendors. Also, we want to develop internal process efficiencies to realize additional savings.”
Poorly managed vendor and raw materials data was impacting Valspar’s buying power
The Valspar team, who sharply focuses on productivity, had an “Aha” moment. “We realized our buying power was limited by the age and quality of available vendor data and raw materials data,” revealed Steve.
The core vendor data and raw materials data that should have been the same across multiple systems wasn’t. Data was often missing or wrong. This made it difficult to calculate the total spend on raw materials. It was also hard to calculate the total cost of expedited freight of raw materials. So, employees used a manual, time-consuming and error-prone process to consolidate vendor data and raw materials data for reporting.
These data issues were getting in the way of achieving their improvement objectives. Valspar needed a data management solution.
Valspar needed a single trusted source of vendor and raw materials data
The team chose Informatica MDM, master data management (MDM) technology. It will be their enterprise hub for vendors and raw materials. It will manage this data centrally on an ongoing basis. With Informatica MDM, Valspar will have a single trusted source of vendor and raw materials data.
Informatica PowerCenter will access data from multiple source systems. Informatica Data Quality will profile the data before it goes into the hub. Then, after Informatica MDM does it’s magic, PowerCenter will deliver clean, consistent, connected and enriched data to target systems.
Better vendor and raw materials data management results in cost savings
Valspar expects to gain the following business benefits:
- Streamline the RFQ process to accelerate raw materials cost savings
- Reduce the total number of raw materials SKUs and vendors
- Increase productivity of staff focused on pulling and maintaining data
- Leverage consistent global data visibly to:
- increase leverage during contract negotiations
- improve acquisition due diligence reviews
- facilitate process standardization and reporting
Valspar’s vision is to tranform data and information into a trusted organizational assets
“Mastering vendor and raw materials data is Phase 1 of our vision to transform data and information into trusted organizational assets,” shared Steve. In Phase 2 the Valspar team will master customer data so they have immediate access to the total purchases of key global customers. In Phase 3, Valspar’s team will turn their attention to product or finished goods data.
Steve ended his presentation with some advice. “First, include your business counterparts in the process as early as possible. They need to own and drive the business case as well as the approval process. Also, master only the vendor and raw materials attributes required to realize the business benefit.”
Want more? Download the Total Supplier Information Management eBook. It covers:
- Why your fragmented supplier data is holding you back
- The cost of supplier data chaos
- The warning signs you need to be looking for
- How you can achieve Total Supplier Information Management
Last time I talked about how benchmark data can be used in IT and business use cases to illustrate the financial value of data management technologies. This time, let’s look at additional use cases, and at how to philosophically interpret the findings.
So here are some additional areas of investigation for justifying a data quality based data management initiative:
- Compliance or any audits data and report preparation and rebuttal (FTE cost as above)
- Excess insurance premiums on incorrect asset or party information
- Excess tax payments due to incorrect asset configuration or location
- Excess travel or idle time between jobs due to incorrect location information
- Excess equipment downtime (not revenue generating) or MTTR due to incorrect asset profile or misaligned reference data not triggering timely repairs
- Equipment location or ownership data incorrect splitting service cost or revenues incorrectly
- Party relationship data not tied together creating duplicate contacts or less relevant offers and lower response rates
- Lower than industry average cross-sell conversion ratio due to inability to match and link departmental customer records and underlying transactions and expose them to all POS channels
- Lower than industry average customer retention rate due to lack of full client transactional profile across channels or product lines to improve service experience or apply discounts
- Low annual supplier discounts due to incorrect or missing alternate product data or aggregated channel purchase data
I could go on forever, but allow me to touch on a sensitive topic – fines. Fines, or performance penalties by private or government entities, only make sense to bake into your analysis if they happen repeatedly in fairly predictable intervals and are “relatively” small per incidence. They should be treated like M&A activity. Nobody will buy into cost savings in the gazillions if a transaction only happens once every ten years. That’s like building a business case for a lottery win or a life insurance payout with a sample size of a family. Sure, if it happens you just made the case but will it happen…soon?
Use benchmarks and ranges wisely but don’t over-think the exercise either. It will become paralysis by analysis. If you want to make it super-scientific, hire an expensive consulting firm for a 3 month $250,000 to $500,000 engagement and have every staffer spend a few days with them away from their day job to make you feel 10% better about the numbers. Was that worth half a million dollars just in 3rd party cost? You be the judge.
In the end, you are trying to find out and position if a technology will fix a $50,000, $5 million or $50 million problem. You are also trying to gauge where key areas of improvement are in terms of value and correlate the associated cost (higher value normally equals higher cost due to higher complexity) and risk. After all, who wants to stand before a budget committee, prophesy massive savings in one area and then fail because it would have been smarter to start with something simpler and quicker win to build upon?
The secret sauce to avoiding this consulting expense and risk is a natural curiosity, willingness to do the legwork of finding industry benchmark data, knowing what goes into them (process versus data improvement capabilities) to avoid inappropriate extrapolation and using sensitivity analysis to hedge your bets. Moreover, trust an (internal?) expert to indicate wider implications and trade-offs. Most importantly, you have to be a communicator willing to talk to many folks on the business side and have criminal interrogation qualities, not unlike in your run-of-the-mill crime show. Some folks just don’t want to talk, often because they have ulterior motives (protecting their legacy investment or process) or hiding skeletons in the closet (recent bad performance). In this case, find more amenable people to quiz or pry the information out of these tough nuts, if you can.
Lastly; if you find ROI numbers, which appear astronomical at first, remember that leverage is a key factor. If a technical capability touches one application (credit risk scoring engine), one process (quotation), one type of transaction (talent management self-service), a limited set of people (procurement), the ROI will be lower than a technology touching multiple of each of the aforementioned. If your business model drives thousands of high-value (thousands of dollars) transactions versus ten twenty-million dollar ones or twenty-million one-dollar ones, your ROI will be higher. After all, consider this; retail e-mail marketing campaigns average an ROI of 578% (softwareprojects.com) and this with really bad data. Imagine what improved data can do just on that front.
I found massive differences between what improved asset data can deliver in a petrochemical or utility company versus product data in a fashion retailer or customer (loyalty) data in a hospitality chain. The assertion of cum hoc ergo propter hoc is a key assumption how technology delivers financial value. As long as the business folks agree or can fence in the relationship, you are on the right path.
What’s your best and worst job to justify someone giving you money to invest? Share that story.
Malcolm Gladwell wrote an article in The New Yorker magazine in January, 2007 entitled “Open Secrets.” In the article, he pointed out that a national-security expert had famously made a distinction between puzzles and mysteries.
Osama bin Laden’s whereabouts were, for many years, a puzzle. We couldn’t find him because we didn’t have enough information. The key to the puzzle, it was assumed, would eventually come from someone close to bin Laden, and until we could find that source, bin Laden would remain at large. In fact, that’s precisely what happened. Al-Qaida’s No. 3 leader, Khalid Sheikh Mohammed, gave authorities the nicknames of one of bin Laden’s couriers, who then became the linchpin to the CIA’s efforts to locate Bin Laden.
By contrast, the problem of what would happen in Iraq after the toppling of Saddam Hussein was a mystery. It wasn’t a question that had a simple, factual answer. Mysteries require judgments and the assessment of uncertainty, and the hard part is not that we have too little information but that we have too much.
This was written before “Big Data” was a household word and it begs the very interesting question of whether organizations and corporations that are, by anyone’s standards, totally deluged with data, are facing puzzles or mysteries. Consider the amount of data that a company like Western Union deals with.
Western Union is a 160-year old company. Having built scale in the money transfer business, the company is in the process of evolving its business model by enabling the expansion of digital products, growth of web and mobile channels, and a more personalized online customer experience. Sounds good – but get this: the company processes more than 29 transactions per seconds on average. That’s 242 million consumer-to-consumer transactions and 459 million business payments in a year. Nearly a billion transactions – a billion! As my six-year-old might say, that number is big enough “to go to the moon and back.” Layer on top of that the fact that the company operates in 200+ countries and territories, and conducts business in 120+ currencies. Senior Director and Head of Engineering Abhishek Banerjee has said, “The data is speaking to us. We just need to react to it.” That implies a puzzle, not a mystery – but only if data scientists are able to conduct statistical modeling and predictive analysis, systematically noting trends in sending and receiving behaviors. Check out what Banerjee and Western Union CTO Sanjay Saraf have to say about it here.
Or consider General Electric’s aggressive and pioneering move into what’s dubbed as the industrial internet. In a white paper entitled “The Case for an Industrial Big Data Platform: Laying the Groundwork for the New Industrial Age,” GE reveals some of the staggering statistics related to the industrial equipment that it manufactures and supports (services comprise 75% of GE’s bottom line):
- A modern wind turbine contains approximately 50 sensors and control loops which collect data every 40 milliseconds.
- A farm controller then receives more than 30 signals from each turbine at 160-millisecond intervals.
- At every one-second interval, the farm monitoring software processes 200 raw sensor data points with various associated properties with each turbine.
Phew! I’m no electricity operations expert, and you probably aren’t either. And most of us will get no further than simply wrapping our heads around the simple fact that GE turbines are collecting a LOT of data. But what the paper goes on to say should grab your attention in a big way: “The key to success for this wind farm lies in the ability to collect and deliver the right data, at the right velocity, and in the right quantities to a wide set of well-orchestrated analytics.” And the paper goes on to recommend that anyone involved in the Industrial Internet revolution strongly consider its talent requirements, with the suggestion that Chief Data officers and/or Data Scientists may be the next critical hires.
Which brings us back to Malcolm Gladwell. In the aforementioned article, Gladwell goes on to pull apart the Enron debacle, and argues that it was a prime example of the perils of too much information. “If you sat through the trial of (former CEO) Jeffrey Skilling, you’d think that the Enron scandal was a puzzle. The company, the prosecution said, conducted shady side deals that no one quite understood. Senior executives withheld critical information from investors…We were not told enough—the classic puzzle premise—was the central assumption of the Enron prosecution.” But in fact, that was not true. Enron employed complicated – but perfectly legal–accounting techniques used by companies that engage in complicated financial trading. Many journalists and professors have gone back and looked at the firm’s regulatory filings, and have come to the conclusion that, while complex and difficult to identify, all of the company’s shenanigans were right there in plain view. Enron cannot be blamed for covering up the existence of its side deals. It didn’t; it disclosed them. As Gladwell summarizes:
“Puzzles are ‘transmitter-dependent’; they turn on what we are told. Mysteries are ‘receiver dependent’; they turn on the skills of the listener.”
I would argue that this extremely complex, fast moving and seismic shift that we call Big Data will favor those who have developed the ability to attune, to listen and make sense of the data. Winners in this new world will recognize what looks like an overwhelming and intractable mystery, and break that mystery down into small and manageable chunks and demystify the landscape, to uncover the important nuggets of truth and significance.
A mid-sized insurer recently approached our team for help. They wanted to understand how they fell short in making their case to their executives. Specifically, they proposed that fixing their customer data was key to supporting the executive team’s highly aggressive 3-year growth plan. (This plan was 3x today’s revenue). Given this core organizational mission – aside from being a warm and fuzzy place to work supporting its local community – the slam dunk solution to help here is simple. Just reducing the data migration effort around the next acquisition or avoiding the ritual annual, one-off data clean-up project already pays for any tool set enhancing data acquisitions, integration and hygiene. Will it get you to 3x today’s revenue? It probably won’t. What will help are the following:
Hard cost avoidance via software maintenance or consulting elimination is the easy part of the exercise. That is why CFOs love it and focus so much on it. It is easy to grasp and immediate (aka next quarter).
Soft cost reduction, like staff redundancies are a bit harder. Despite them being viable, in my experience very few decision makers want work on a business case to lay off staff. My team had one so far. They look at these savings as freed up capacity, which can be re-deployed more productively. Productivity is also a bit harder to quantify as you typically have to understand how data travels and gets worked on between departments.
However, revenue effects are even harder and esoteric to many people as they include projections. They are often considered “soft” benefits, although they outweigh the other areas by 2-3 times in terms of impact. Ultimately, every organization runs their strategy based on projections (see the insurer in my first paragraph).
The hardest to quantify is risk. Not only is it based on projections – often from a third party (Moody’s, TransUnion, etc.) – but few people understand it. More often, clients don’t even accept you investigating this area if you don’t have an advanced degree in insurance math. Nevertheless, risk can generate extra “soft” cost avoidance (beefing up reserve account balance creating opportunity cost) but also revenue (realizing a risk premium previously ignored). Often risk profiles change due to relationships, which can be links to new “horizontal” information (transactional attributes) or vertical (hierarchical) from parent-child relationships of an entity and the parent’s or children’s transactions.
Given the above, my initial advice to the insurer would be to look at the heartache of their last acquisition, use a benchmark for IT productivity from improved data management capabilities (typically 20-26% – Yankee Group) and there you go. This is just the IT side so consider increasing the upper range by 1.4x (Harvard Business School) as every attribute change (last mobile view date) requires additional meetings on a manager, director and VP level. These people’s time gets increasingly more expensive. You could also use Aberdeen’s benchmark of 13hrs per average master data attribute fix instead.
You can also look at productivity areas, which are typically overly measured. Let’s assume a call center rep spends 20% of the average call time of 12 minutes (depending on the call type – account or bill inquiry, dispute, etc.) understanding
- Who the customer is
- What he bought online and in-store
- If he tried to resolve his issue on the website or store
- How he uses equipment
- What he cares about
- If he prefers call backs, SMS or email confirmations
- His response rate to offers
- His/her value to the company
If he spends these 20% of every call stringing together insights from five applications and twelve screens instead of one frame in seconds, which is the same information in every application he touches, you just freed up 20% worth of his hourly compensation.
Then look at the software, hardware, maintenance and ongoing management of the likely customer record sources (pick the worst and best quality one based on your current understanding), which will end up in a centrally governed instance. Per DAMA, every duplicate record will cost you between $0.45 (party) and $0.85 (product) per transaction (edit touch). At the very least each record will be touched once a year (likely 3-5 times), so multiply your duplicated record count by that and you have your savings from just de-duplication. You can also use Aberdeen’s benchmark of 71 serious errors per 1,000 records, meaning the chance of transactional failure and required effort (% of one or more FTE’s daily workday) to fix is high. If this does not work for you, run a data profile with one of the many tools out there.
If standardization of records (zip codes, billing codes, currency, etc.) is the problem, ask your business partner how many customer contacts (calls, mailing, emails, orders, invoices or account statements) fail outright and/or require validation because of these attributes. Once again, if you apply the productivity gains mentioned earlier, there are you savings. If you look at the number of orders that get delayed in form of payment or revenue recognition and the average order amount by a week or a month, you were just able to quantify how much profit (multiply by operating margin) you would be able to pull into the current financial year from the next one.
The same is true for speeding up the introduction or a new product or a change to it generating profits earlier. Note that looking at the time value of funds realized earlier is too small in most instances especially in the current interest environment.
If emails bounce back or snail mail gets returned (no such address, no such name at this address, no such domain, no such user at this domain), e(mail) verification tools can help reduce the bounces. If every mail piece (forget email due to the miniscule cost) costs $1.25 – and this will vary by type of mailing (catalog, promotion post card, statement letter), incorrect or incomplete records are wasted cost. If you can, use fully loaded print cost incl. 3rd party data prep and returns handling. You will never capture all cost inputs but take a conservative stab.
If it was an offer, reduced bounces should also improve your response rate (also true for email now). Prospect mail response rates are typically around 1.2% (Direct Marketing Association), whereas phone response rates are around 8.2%. If you know that your current response rate is half that (for argument sake) and you send out 100,000 emails of which 1.3% (Silverpop) have customer data issues, then fixing 81-93% of them (our experience) will drop the bounce rate to under 0.3% meaning more emails will arrive/be relevant. This in turn multiplied by a standard conversion rate (MarketingSherpa) of 3% (industry and channel specific) and average order (your data) multiplied by operating margin gets you a benefit value for revenue.
If product data and inventory carrying cost or supplier spend are your issue, find out how many supplier shipments you receive every month, the average cost of a part (or cost range), apply the Aberdeen master data failure rate (71 in 1,000) to use cases around lack of or incorrect supersession or alternate part data, to assess the value of a single shipment’s overspend. You can also just use the ending inventory amount from the 10-k report and apply 3-10% improvement (Aberdeen) in a top-down approach. Alternatively, apply 3.2-4.9% to your annual supplier spend (KPMG).
You could also investigate the expediting or return cost of shipments in a period due to incorrectly aggregated customer forecasts, wrong or incomplete product information or wrong shipment instructions in a product or location profile. Apply Aberdeen’s 5% improvement rate and there you go.
Consider that a North American utility told us that just fixing their 200 Tier1 suppliers’ product information achieved an increase in discounts from $14 to $120 million. They also found that fixing one basic out of sixty attributes in one part category saves them over $200,000 annually.
So what ROI percentages would you find tolerable or justifiable for, say an EDW project, a CRM project, a new claims system, etc.? What would the annual savings or new revenue be that you were comfortable with? What was the craziest improvement you have seen coming to fruition, which nobody expected?
Next time, I will add some more “use cases” to the list and look at some philosophical implications of averages.
As adjunct university faculty, I get to talk to students about how business strategy increasingly depends upon understanding how to leverage information. To make discussion more concrete, I share with students the work of Alvin Toffler. In The Third Wave, Toffler asserts that we live in a world where competition will increasingly take place upon the currency and usability of information.
In a recent interview, Toffler said that “given the acceleration of change; companies, individuals, and governments base many of their daily decisions on obsoledge—knowledge whose shelf life has expired.” He continues by stating that “companies everywhere are trying to put a price on certain forms of intellectual property. But if…knowledge is at the core of the money economy, than we need to understand knowledge much better than we do now. And tiny insights can yield huge outputs”.
Driving better information management in the information age
To me, this drives to three salient conclusions for information age businesses:
- Information needs to drive further down organizations because top decision makers do not have the background to respond at the pace of change.
- Information needs to be available faster which means that we need to reducing the processing time for structure and unstructured information sources.
- Information needs to be available when the organization is ready for it. For multinational enterprises this means “Always On” 24/7 across multiple time zones on any device.
Effective managers today are effective managers of people and information
Effective managers today are effective managers of information. Because processing may take too much time, Toffler’s remarks suggest to me we need to consider human information—the ideas and communications we share every day—within the mix of getting access to the right information when it is needed and where it is needed. Now more than ever is the time for enterprises to ensure their decision makers have the timely information to make better business decisions when they are relevant. This means that unstructured data, a non-trivial majority of business information, needs to be made available to business users and related to existing structured sources of data.
Derick Abell says that “for (management) control to be effective, data must be timely and provided at interval that allows effective intervention”. Today this is a problem for most information businesses. As I see it, information optimization is the basis of powering the enterprise through “Third Wave” business competition. Organizations that have the “right to win” will have as a core capability better-than-class access to current information for decision makers.
Putting in place a winning information management strategy
If you talk to CIOs today, they will tell you that they are currently facing 4 major information age challenges.
- Mobility—Enabling their users to view data anytime, anyplace, and any device
- Information Trust—Making data dependable enough for business decisions as well as governing data across all business systems.
- Competing on Analytics—Getting information to business users fast enough to avoid Toffler’s Obsoledge.
- New and Big Data Sources—Connecting existing data to new value added sources of data.
Some information age
Lots of things, however, get in the way of delivering on the promises of the Information Age. Our current data architecture is siloed, fragile, and built upon layer after layer of spaghetti code integrations. Think about what is involved just to cobble together data on a company’s supply chain. A morass of structured data systems have vendor and transaction records locked up in application databases and data warehouses all over the extended enterprise. So it is not amazing that enterprises struggle to put together current, relevant data to run their businesses upon. Functions like finance depend largely upon manual extracts being massaged and integrated in spreadsheets because of concern over the quality of data being provided by financial systems. Some information age!
How do we connect to new sources of data?
At the same time, many are trying today to extend the information architecture to add social media data, mobile location data, and even machine data. Much of this data is not put together in the same way as data in an application database or data warehouse. However, being able to relate this data to existing data sources can yield significant benefits. Think about the potential benefit of being able to relate social interactions and mobile location data to sales data or to relate machine data to compliance data.
A big problem is many of these new data types potentially have even more data quality gaps than historical structured data systems. Often the signal to noise for this data can be very low for this reason. But this data can be invaluable to business decision making. For this reason, this data needs to be cleaned up and related to older data sources. Finally, it needs to be provided to business users in whatever manner they want to consume it.
How then do we fix the Information Age?
Enabling the kind of Information Age that Toffler imagined requires two things. Enterprises fix their data management and enable the information intelligence needed to drive real business competitive advantage. Fixing data management involves delivering good data that business users can safely make decisions from. It, also, involves ensuring that data once created is protected. CFOs that we have talked to say Target was a watershed event for them—something that they expect will receive more and more auditing attention.
We need at the same time to build the connection between old data sources and new data sources. And this needs to not take as long as in the past to connect data. Delivery needs to happen faster so business problems can be recognized and solved more quickly. Users need to get access to data when and where they need it.
With data management fixed, data intelligence needs to provide business users the ability to make sense out of things they find in the data. Business users need as well to be able to search and find data. They, also, need self-service so they can combine existing and new unstructured data sources to test data interrelationship hypothesis. This means the ability to assemble data and put it together and do it from different sources at different times. Simply put this is about data orchestration without any preconceived process. And lastly, business users need the intelligence to automatically sense and respond to changes as new data is collecting.
Tiny insights can yield huge outputs
Obviously, there is a cost to solving our information age issues, but it is important to remember what Toffler says. “Tiny insights can yield huge outputs”. In other words, the payoff is huge for shaking off the shackles of our early information age business architecture. And those that do this will increasingly have the “right to win” against their competitors as they use information to wring every last drop of value from their business processes.
Solution Brief: The Intelligent Data Platform
A few days ago, I came across a post, 5 C’s of MDM (Case, Content, Connecting, Cleansing, and Controlling), by Peter Krensky, Sr. Research Associate, Aberdeen Group and this response by Alan Duncan with his 5 C’s (Communicate, Co-operate, Collaborate, Cajole and Coerce). I like Alan’s list much better. Even though I work for a product company specializing in information management technology, the secret to successful enterprise information management (EIM) is in tackling the business and organizational issues, not the technology challenges. Fundamentally, data management at the enterprise level is an agreement problem, not a technology problem.
So, here I go with my 5 C’s: (more…)
I recently wrapped up two overseas trips; one to Central America and another to South Africa. As such, I had the opportunity to meet with a national bank and a regional retailer. It prompted me to ask the question: Does location matter in emerging markets?
I wish I could tell you that there was a common theme on how firms in the same sector or country (even city) treat data on a philosophical or operational level but I cannot. It is such a unique experience every time as factors like ownership history, regulatory scrutiny, available/affordable skill set and past as well as current financial success create a unique grey pattern rather than a comfortable black and white separation. This is even more obvious when I mix in recent meetings I had with North American organizations in the same sectors.
Banking in Latin vs North America
While a national bank in Latin America may seem lethargic, unimaginative and unpolished at first, you can feel the excitement when they can conceive, touch and play with the potential of new paradigms, like becoming data-driven. Decades of public ownership did not seem to have stifled their willingness to learn and improve. On the other side, there is a stock market-listed, regional US bank and half the organization appears to believe in meddling along without expert IT knowledge, which reduced adoption and financial success in past projects. Back office leadership also firmly believes in “relationship management” over data-driven “value management”.
To quote a leader in their finance department, “we don’t believe that knowing a few more characteristics about a client creates more profit….the account rep already knows everything about them and what they have and need”. Then he said, “Not sure why the other departments told you there are issues. We have all this information but it may not be rolled out to them yet or they have no license to view it to date.” This reminded me of the “All Quiet on the Western Front” mentality. If it is all good over here, why are most people saying it is not? Granted; one more attribute may not tip the scale to higher profits but a few more and their historical interrelationship typically does.
As an example; think about the correlation of average account balance fluctuations, property sale, bill pay account payee set ups, credit card late charges and call center interactions over the course of a year.
The Latin American bankers just said, “We have no idea what we know and don’t know…but we know that even long standing relationships with corporate clients are lacking upsell execution”. In this case, upsell potential centered on wire transfer SWIFT message transformation to their local standard they report of and back. Understanding the SWIFT message parameters in full creates an opportunity to approach originating entities and cutting out the middleman bank.
Retailing in Africa vs Europe
The African retailer’s IT architects indicated that customer information is centralized and complete and that integration is not an issue as they have done it forever. Also, consumer householding information is not a viable concept due to different regional interpretations, vendor information is brand specific and therefore not centrally managed and event based actions are easily handled in BizTalk. Home delivery and pickup is in its infancy.
The only apparent improvement area is product information enrichment for an omnichannel strategy. This would involve enhancing attribution for merchandise demand planning, inventory and logistics management and marketing. Attributes could include not only full and standardized capture of style, packaging, shipping instructions, logical groupings, WIP vs finished goods identifiers, units of measure, images and lead times but also regional cultural and climate implications.
However, data-driven retailers are increasingly becoming service and logistics companies to improve wallet share, even in emerging markets. Look at the successful Russian eTailer Ozon, which is handling 3rd party merchandise for shipping and cash management via a combination of agency-style mom & pop shops and online capabilities. Having good products at the lowest price alone is not cutting it anymore and it has not for a while. Only luxury chains may be able to avoid this realization for now. Store size and location come at a premium these days. Hypermarkets are ill-equipped to deal with high-profit specialty items. Commercial real estate vacancies on British high streets are at a high (Economist, July 13, 2014) and footfall is at a seven-year low. The Centre for Retail Research predicts that 20% of store locations will close over the next five years.
If specialized, high-end products are the most profitable, I can (test) sell most of them online or at least through fewer, smaller stores saving on carrying cost. If my customers can then pick them up and return them however they want (store, home) and I can reduce returns from normally 30% (per the Economist) to fewer than 10% by educating and servicing them as unbureaucratically as possible, I just won the semifinals. If I can then personalize recommendations based on my customers’ preferences, life style events, relationships, real-time location and reward them in a meaningful way, I just won the cup.
Emerging markets may seem a few years behind but companies like Amazon or Ozon have shown that first movers enjoy tremendous long-term advantages.
So what does this mean for IT? Putting your apps into the cloud (maybe even outside your country) may seem like an easy fix. However, it may not only create performance and legal issues but also unexpected cost to support decent SLA terms. Does your data support transactions for higher profits today to absorb this additional cost of going into the cloud? Focus on transactional applications and their management obfuscates the need for a strong backbone for data management, just like the one you built for your messaging and workflows ten years ago. Then you can tether all the fancy apps to it you want.
Have any emerging markets’ war stories or trends to share? I would love to hear them. Stay tuned for future editions of this series.