Category Archives: Enterprise Data Management
Established in Northwestern United States, North 40 Outfitters, a family owned and operated business, has been outfitting the hardworking and hard playing populace of the region. Understanding the diverse needs of its customers, hardworking people, North 40 Outfitters carries everything from fencing for cattle and livestock to tools and trailers. They have gear for camping and hunting—even fly fishing.
Named after the Homestead Act of 1862, an event with strong significance in the region, North 40 Outfitters heritage is built on its community involvement and support of local small businesses. The company’s 700 employees could be regarded as family. At this year’s Thanksgiving, every employee was given a locally raised free range turkey to bring home. Furthermore, true to Black Friday’s shopping experience, North 40 Outfitters opened its door. Eschewing the regular practice of open as early as 3 AM, North 40 Outfitters opened at the reasonable 7 o’clock hour. They offered patrons donuts as well as coffee obtained from a local roaster.
North 40 Outfitters aims to be different. They achieve differentiation by being data driven. While the products they sell cannot be sourced exclusively from local sources, their experience aims to do exactly that.
Prior to operating under the name North 40 Outfitters, the company ran under the banner of “Big R”, which was shared with several other members of the same buying group. The decision to change the name to North 40 Outfitters was the result of a move into the digital realm— they needed a name to distinguish themselves. Now as North 40 Outfitters, they can focus on what matters rather than having to deal with the confusion of a shared name. They would now provide the “local store” experience, while investing in their digital strategy as a means to do business online and bring the unique North 40 Outfitters experience and value nationwide.
With those organizational changes taking place, lay an even greater challenge. With over 150,000 SKUs and no digital database for their product information, North 40 Outfitters had to find a solution and build everything from the ground up. Moreover, with customers demanding a means to buy products online, especially customers living in rural areas, it became clear that North 40 Outfitters would have to address its data concerns.
Along with the fresh rebrand and restructure, North 40 Outfitters needed to tame their product information situation, a critical step conducive to building their digital product database and launching their ecommerce store.
North 40 Outfitters was clear about the outcome of the recent rebranding and they knew that investments needed to be taken if they were to add value to their existing business. Building the capabilities to take their business to new channels, ecommerce in this case, meant finding the best solution to start on the right foot. Consequently, wishing to become master of their own data, for both online and in-store uses, North 40 Outfitters determined that they needed a PIM application that would act as a unique data information repository.
It’s important to note that North 40 Outfitters environment is not typical to that of traditional retailers. The difference can be found in the large variation of product type they sell. Some of their suppliers have local, boutique style production scales, while some are large multi-national distributors. Furthermore, a large portion of North 40 Outfitters customers live in rural regions, in some cases their stores are a day’s drive away. With the ability to leverage both a PIM and an ecommerce solution North 40 Outfitters is now a step closer to outfitting everyone in the Northwestern region.
It is still very early to talk about results, since North 40 Outfitters has only recently entered the implementation phase. What can be said is that they are very excited. Having reclaimed their territory, and equipped with a PIM solution and an ecommerce solution they have all the right tools to till and plow the playing field.
The meaning of North 40 Outfitters
To the uninitiated the name North 40 Outfitters might not mean much. However, there is a lot of local heritage and history standing behind this newly rebranded name. North 40 is derived from the Homestead Act of 1862. The Act refers to the “North forty”, to the Northern most block of the homesteader’s property. To this day, this still holds significance to the local community. The second half of the brand: “Outfitters” is about the company’s focus on the company ability to outfit its customers both for work and play. On the one hand, you can visit North 40 Outfitters to purchase goods aimed at running your ranch, such as fencing material, horse related goods or quality tools. At the same time, you can buy camping and backpacking goods—they even sell ice fishing huts.
North 40 Outfitters ensures their customers have what they need to work the land, get back from it and ultimately go out and play just as hard if not harder.
Happy Holidays, Happy HoliData
In case you have missed our #HappyHoliData series on Twitter and LinkedIn, I decided to provide a short summary of best practices which are unleashing information potential. Simply scroll and click on the case study which is relevant for you and your business. The series touches on different industries and use cases. But all have one thing in common: All consider information quality as key value to their business to deliver the right services or products to the right customer.
Thanks a lot to all my great teammates, who made this series happen.
Happy Holidays, Happy HoliData.
The other day I ran across an article on CMO.com from a few months ago entitled “Total Customer Value Trumps Simple Loyalty in Digital World”. It’s a great article, so I encourage you to go take a look, but the basic premise is that loyalty does not necessarily equal value in today’s complicated consumer environment.
Customers can be loyal for a variety of reasons as the author Samuel Greengard points out. One of which may be that they are stuck with a certain product or service because they believe there is no better alternative available. I know I can relate to this after a recent series of less-than-pleasant experiences with my bank. I’d like to change banks, but frankly they’re all about the same and it just isn’t worth the hassle. Therefore, I’m loyal to my unnamed bank, but definitely not an advocate.
The proverbial big fish in today’s digital world, according to the author, are customers who truly identify with the brand and who will buy the company’s products eagerly, even when viable alternatives exist. These are the customers who sing the brand’s praises to their friends and family online and in person. These are the customers who write reviews on Amazon and give your product 5 stars. These are the customers who will pay markedly more just because it sports your logo. And these are the customers whose voices hold weight with their peers because they are knowledgeable and passionate about the product. I’m sure we all have a brand or two that we’re truly passionate about.
Total Customer Value in the Pool
My 13 year old son is a competitive swimmer and will only use Speedo goggles – ever – hands down – no matter what. He wears Speedo t-shirts to show his support. He talks about how great his goggles are and encourages his teammates to try on his personal pair to show them how much better they are. He is a leader on his team, so when newbies come in and see him wearing these goggles and singing their praises, and finishing first, his advocacy holds weight. I’m sure we have owned well over 30 pair of Speedo goggles over the past 4 years at $20 a pop – and add in the T-Shirts and of course swimsuits – we probably have a historical value of over $1000 and a potential lifetime value of tens of thousands (ridiculous I know!). But if you add in the influence he’s had over others, his value is tremendously more – at least 5X.
This is why data is king!
I couldn’t agree more that total customer value, or even total partner or total supplier value, is absolutely the right approach, and is a much better indicator of value. But in this digital world of incredible data volumes and disparate data sources & systems, how can you really know what a customer’s value is?
The marketing applications you probably already use are great – there are so many great automation, web analytics, and CRM systems around. But what fuels these applications? Your data.
Most marketers think that data is the stuff that applications generate or consume. As if all data is pretty much the same. In truth, data is a raw ingredient. Data-driven marketers don’t just manage their marketing applications, they actively manage their data as a strategic asset.
How are you using data to analyze and identify your influential customers? Can you tell that a customer bought their fourth product from your website, and then promptly tweeted about the great deal they got on it? Even more interesting, can you tell that that five of their friends followed the link, 1 bought the same item, 1 looked at it but ended up buying a similar item, and 1 put it in their cart but didn’t buy it because it was cheaper on another website? And more importantly, how can you keep this person engaged so they continue their brand preference – so somebody else with a similar brand and product doesn’t swoop in and do it first? And the ultimate question… how can you scale this so that you’re doing this automatically within your marketing processes, with confidence, every time?
All marketers need to understand their data – what exists in your information ecosystem , whether it be internally or externally. Can you even get to the systems that hold the richest data? Do you leverage your internal customer support/call center records? Is your billing /financial system utilized as a key location for customer data? And the elephant in the room… can you incorporate the invaluable social media data that is ripe for marketers to leverage as an automated component of their marketing campaigns?
This is why marketers need to care about data integration…
Even if you do have access to all of the rich customer data that exists within and outside of your firewalls, how can you make sense of it? How can you pull it together to truly understand your customers… what they really buy, who they associate with, and who they influence. If you don’t, then you’re leaving dollars, and more importantly, potential advocacy and true customer value, on the table.
This is why marketers need to care about achieving a total view of their customers and prospects…
And none of this matters if the data you are leveraging is plain incorrect or incomplete. How often have you seen some analysis on an important topic, had that gut feeling that something must be wrong, and questioned the data that was used to pull the report? The obvious data quality errors are really only the tip of the iceberg. Most of the data quality issues that marketers face are either not glaringly obvious enough to catch and correct on the spot, or are baked into an automated process that nobody has the opportunity to catch. Making decisions based upon flawed data inevitably leads to poor decisions.
This is why marketers need to care about data quality.
So, as the article points out, don’t just look at loyalty, look at total customer value. But realize, that this is easier said than done without a focusing in on your data and ensuring you have all of the right data, at the right place, in the right format, right away.
Now… Brand advocates, step up! Share with us your favorite story. What brands do you love? Why? What makes you so loyal?
Building an Enterprise Data Hub with proper Data IntegrationData flows into the enterprise from many sources, in many formats, sizes, and levels of complexity. And as enterprise architectures have evolved over the years, traditional data warehouses have become less of a final staging center for data, but rather, one component of the enterprise that interfaces with significant data flows. But since data warehouses should focus on being powerful engines for high value analytics, they should not be the central hub for data movement and data preparation (e.g. ETL/ELT), especially for the newer data types–such as social media, clickstream data, sensor data, internet-of-things-data, etc.–that are in use today.
When you start seeing data warehouse capacity consumed too quickly and performance degradation where end users are complaining about slower response times, and you risk not meeting your service-level agreements, then it might be time to consider an enterprise data hub (EDH). With an EDH, especially one built on Apache™ Hadoop®, you can plan a strategy around data warehouse optimization to get better use out of your entire enterprise architecture.
Of course, whenever you add another new technology to your data center, you care about interoperability. And since many systems in today’s architectures interoperate via data flows, it’s clear that sophisticated data integration technologies will be an important part of your EDH strategy. Today’s big data presents new challenges as relates to a wide variety of data types and formats, and the right technologies are needed to glue all the pieces together, whether those pieces are data warehouses, relational databases, Hadoop, or NoSQL databases.
Choosing a Data Integration Solution
Data integration software, at a high level, has one broad responsibility: to help you process and prepare your data with the right technology. This means it has to get your data to the right place in the right format in a timely manner. So it actually includes many tasks, but the end result is that timely, trusted data can be used for decision-making and risk management throughout the enterprise. You end up with a complete, ready-for-analysis picture of your business, as opposed to segmented snapshots based on a limited data set.
When evaluating a data integration solution for the enterprise, look for:
- Ease of use to boost developer productivity
- A proven track record in the industry
- Widely available technology expertise
- Experience with production deployments with newer technologies like Hadoop
- Ability to reuse data pipelines across different technologies (e.g. data warehouse, RDBMS, Hadoop, and other NoSQL databases)
Data integration is only part of the story. When you’re depending on data to drive business decisions and risk management, you clearly want to ensure the data is reliable. Data governance, data lineage, data quality, and data auditing remain as important topics in an EDH. Oftentimes, data privacy regulatory demands must be met, and the enterprise’s own intellectual property must be protected from accidental exposure.
To help ensure that data is sound and secure, look for a solution that provides:
- Centralized management and control
- Data certification prior to publication, transparent data and integration processes, and the ability to track data lineage
- Granular security, access controls, and data masking to protect data both in transit and at the source to prevent unauthorized access to specific data sets
Informatica is the data integration solution selected by many enterprises. Informatica’s family of enterprise data integration, data quality, and other data management products can manage data — of any format, complexity level, or size –from any business system, and then deliver that data across the enterprise at the desired speed.
Watch the latest Gartner video to see Todd Goldman, Vice President and General Manager for Enterprise Data Integration at Informatica, as well as executives from Cisco and MapR, give their perspective on how businesses today can gain even more value from big data.
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.
Information workers deal with information, or in other words, data. They use that data to do their jobs. They make decisions in business with that data. They impact the lives of their clients.
Many years ago, I was part of a formative working group researching information worker productivity. The idea was to create an index like Labor Productivity indexes. It was to be aimed at information worker productivity. By this I mean the analysts, accountants, actuaries, underwriters and statisticians. These are business information workers. How productive are they? How do you measure their output? How do you calculate an economic cost of more or less productive employees? How do you quantify the “soft” costs of passing work on to information workers? The effort stalled in academia, but I learned a few key things. These points underline the nature of an information worker and impacts to their productivity.
- Information workers need data…and lots of it
- Information workers use applications to view and manipulate data to get the job done
- Degradation, latency or poor ease of use in any of items 1 and 2 have a direct impact on productivity
- Items 1 and 2 have a direct correlation to training cost, output and (wait for it) employee health and retention
It’s time to make a super bold statement. It’s time to maximize your investment in DATA. And past time to de-emphasize investments in applications! Stated another way, applications come and go, but data lives forever.
My five-year old son is addicted to his iPad. He’s had one since he was one-year old. At about the age of three he had pretty much left off playing Angry Birds. He started reading Wikipedia. He started downloading apps from the App Store. He wanted to learn about string theory, astrophysics and plate tectonics. Now, he scares me a little with his knowledge. I call him my little Sheldon Cooper. The apps that he uses for research are so cool. The way that they present the data, the speed and depth are amazing. As soon as he’s mastered one, he’s on to the next one. It won’t be long before he’s going to want to program his own apps. When that day comes, I’ll do whatever it takes to make him successful.
And he’s not alone. The world of the “selfie-generation” is one of rapid speed. It is one of application proliferation and flat out application “coolness”. High school students are learning iOS programming. They are using cloud infrastructure to play games and run experiments. Anyone under the age of 27 has been raised in a mélange of amazing data-fueled computing and mobility.
This is your new workforce. And on their first day of their new career at an insurance company or large bank, they are handed an aging recycled workstation. An old operating system follows and mainframe terminal sessions. Then comes rich-client and web apps circa 2002. And lastly (heaven forbid) a Blackberry. Now do you wonder if that employee will feel empowered and productive? I’ll tell you now, they won’t. All that passion they have for viewing and interacting with information will disappear. It will not be enabled in their new work day. An outright information worker revolution would not surprise me.
And that is exactly why I say that it’s time to focus on data and not on applications. Because data lives on as applications come and go. I am going to coin a new phrase. I call this the Empowered Selfie Formula. The Empowered Selfie Formula is a way in which the focus on data liberates information workers. They become free to be more productive in today’s technology ecosystem.
Enable a BYO* Culture
Many organizations have been experimenting with Bring Your Own Device (BYOD) programs. Corporate stipends that allow employees to buy the computing hardware of their choice. But let’s take that one step further. How about a Bring Your Own Application program? How about a Bring Your Own Codebase program? The idea is not so far-fetched. There are so many great applications for working with information. Today’s generation is learning about coding applications at a rapid pace. They are keen to implement their own processes and tools to “get the job done”. It’s time to embrace that change. Allow your information workers to be productive with their chosen devices and applications.
Empower Social Sharing
Your information workers are now empowered with their own flavors of device and application productivity. Let them share it. The ability to share success, great insights and great apps is engrained into the mindset of today’s technology users. Companies like Tableau have become successful based on the democratization of business intelligence. Through enabling social sharing, users can celebrate their successes and cool apps with colleagues. This raises the overall levels of productivity as a grassroots movement. Communities of best practices begin to emerge creating innovation where not previously seen.
As an organization it is important to measure success. Find ways to capture key metrics in productivity of this new world of data-fueled information work. Each information worker will typically be able to track trends in their output. When they show improvement, celebrate that success.
Invest in “Cool”
With a new BYO* culture, make the investments in cool new things. Allow users to spend a few dollars here and there for training online or in-person. There they can learn new things will make them more productive. It will also help with employee retention. With small investment larger ROI can be realized in employee health and productivity.
Foster Healthy Competition
Throughout history, civilizations that fostered healthy competition have innovated faster. The enterprise can foster healthy competition on metrics. Other competition can be focused on new ways to look at information, valuable insights, and homegrown applications. It isn’t about a “best one wins” competition. It is a continuing round of innovation winners with lessons learned and continued growth. These can also be centered on the social sharing and community aspects. In the end it leads to a more productive team of information workers.
Revitalize Your Veterans
Naturally those information workers who are a little “longer in the tooth” may feel threatened. But this doesn’t need to be the case. Find ways to integrate them into the new culture. Do this through peer training, knowledge transfer, and the data items listed below. In the best of cases, they too will crave this new era of innovation. They will bring a lot of value to the ecosystem.
There is a catch. In order to realize success in the formula above, you need to overinvest in data and data infrastructure. Perhaps that means doing things with data that only received lip service in the past. It is imperative to create a competency or center of excellence for all things data. Trusting your data centers of excellence activates your Empowered Selfie Formula.
You are going to have users using and building new apps and processing data and information in new and developing ways. This means you need to trust your data. Your data governance becomes more important. Everything from metadata, data definition, standards, policies and glossaries need to be developed. In this way the data that is being looked at can be trusted. Chief Data Officers should put into place a data governance competency center. All data feeding and coming from new applications is inspected regularly for adherence to corporate standards. Remember, it’s not about the application. It’s about what feeds any application and what data is generated.
Very much a part of data governance is the quality of data in the organization. Also adhering to corporate standards. These standards should dictate cleanliness, completeness, fuzzy logic and standardization. Nothing frustrates an information worker more than building the coolest app that does nothing due to poor quality data.
Data needs to be in the right place at the right time. Any enterprise data takes a journey from many places and to many places. Movement of data that is governed and has met quality standards needs to happen quickly. We are in a world of fast computing and massive storage. There is no excuse for not having data readily available for a multitude of uses.
And finally, make sure to secure your data. Regardless of the application consuming your information, there may be people that shouldn’t see the data. Access control, data masking and network security needs to be in place. Each application from Microsoft Excel to Informatica Springbok to Tableau to an iOS developed application will only interact with the information it should see.
The changing role of an IT group will follow close behind. IT will essentially become the data-fueled enablers using the principles above. IT will provide the infrastructure necessary to enable the Empowered Selfie Formula. IT will no longer be in the application business, aside from a few core corporation applications as a necessary evil.
Achieving a competency in the items above, you no longer need to worry about the success of the Empowered Selfie Formula. What you will have is a truly data-fueled enterprise. There will be a new class of information workers enabled by a data-fueled competency. Informatica is thrilled to be an integral part of the realization that data can play in your journey. We are energized to see the pervasive use of data by increasing numbers of information workers. The are creating new and better ways to do business. Come and join a data-fueled world with Informatica.
With the increasing importance of enterprise analytics, the question becomes who should own the analytics and data agenda. This question really matters today because, according to Thomas Davenport, “business processes are among the last remaining points of differentiation.” For this reason, Davenport even suggests that businesses that create a sustainable right to win use analytics to “wring every last drop of value from their processes”.
The CFO is the logical choice?
In talking with CIOs about both enterprise analytics and data, they are clear that they do not want to become their company’s data steward. They insist instead that they want to be an enabler of the analytics and data function. So what business function then should own enterprise analytics and data? Last week an interesting answer came from a CFO Magazine Article by Frank Friedman. Frank contends that CFOs are “the logical choice to own analytics and put them to work to serve the organization’s needs”.
To justify his position, Frank made the following claims:
- CFOs own most of the unprecedented quantities of data that businesses create from supply chains, product processes, and customer interactions
- Many CFOs already use analytics to address their organization’s strategic issues
- CFOs uniquely can act as a steward of value and an impartial guardian of truth across the organizations. This fact gives them the credibility and trust needed when analytics produce insights that effectively debunk currently accepted wisdom
Frank contends as well that owning the analytics agenda is a good thing because it allows CFOs to expand their strategic leadership role in doing the following:
- Growing top line revenue
- Strengthening their business ties
- Expanding the CFO’s influence outside the finance function.
Frank suggests as well that analytics empowers the CFO to exercise more centralized control of operational business decision making. The question is what do other CFOs think about Frank’s position?
CFOs clearly have an opinion about enterprise analytics and data
A major Retail CFO says that finance needs to own “the facts for the organization”—the metrics and KPIs. And while he honestly admits that finance organizations in the past have not used data well, he claims finance departments need to make the time to become truly data centric. He said “I do not consider myself a data expert, but finance needs to own enterprise data and the integrity of this data”. This CFO claims as well that “finance needs to use data to make sure that resources are focused on the right things; decisions are based on facts; and metrics are simple and understandable”. A Food and Beverage CFO agrees with the Retail CFO by saying that almost every piece of data is financial in one way or another. CFOs need to manage all of this data since they own operational performance for the enterprise. CFOs should own the key performance indicators of the business.
CIOs should own data, data interconnect, and system selection
A Healthcare CFO said he wants, however, the CIO to own data systems, data interconnect, and system selection. However, he believes that the finance organization is the recipient of data. “CFOs have a major stake in data. CFOs need to dig into operational data to be able to relate operations to internal accounting and to analyze things like costs versus price”. He said that “the CFOs can’t function without good operational data”.
An Accounting Firm CFO agreed with the Healthcare CFO by saying that CIOs are a means to get data. She said that CFOs need to make sense out of data in their performance management role. CFOs, therefore, are big consumers of both business intelligence and analytics. An Insurance CFO concurred by saying CIOs should own how data is delivered.
CFOs should be data validators
The Insurance CFOs said, however, CFOs need to be validators of data and reports. They should, as a result, in his opinion be very knowledgeable on BI and Analytics. In other words, CFOs need to be the Underwriters Laboratory (UL) for corporate data.
Now it is your chance
So the question is what do you believe? Does the CFO own analytics, data, and data quality as a part of their operational performance role? Or is it a group of people within the organization? Please share your opinions below.
Solution Brief: The Intelligent Data Platform
CFOs Move to Chief Profitability Officer
CFOs Discuss Their Technology Priorities
The CFO Viewpoint upon Data
How CFOs can change the conversation with their CIO?
New type of CFO represents a potent CIO ally
Competing on Analytics
The Business Case for Better Data Connectivity
ERP systems were a true competitive advantage 20+ years ago, but not so today. ERP systems are a tool that gave people the best view into their business, but that is when there really were only ERP systems and Databases, but today that critical data resides in so many other areas. There are several reasons why ERP systems act as a data trap: technical factors, out of date management theory, and big data trends. First, let’s talk about management theory.
There are two fundamental concepts that have been driving much of the strategic planning in modern organizations in recent decades. The idea of economies of scale is deeply embedded in our thinking. The concept was first introduced by Adam Smith in the 18th century and reinforced throughout the 20th century by contemporaries such as Bruce Henderson. In 1968 Henderson wrote “”Costs characteristically decline by 20-30% in real terms each time accumulated experience doubles.“ The basic idea is that bigger is better. (more…)
The title of this article may seem counterintuitive, but the reality is that the business doesn’t care about data. They care about their business processes and outcomes that generate real value for the organization. All IT professionals know there is huge value in quality data and in having it integrated and consistent across the enterprise. The challenge is how to prove the business value of data if the business doesn’t care about it. (more…)
Every fall Informatica sales leadership puts together its strategy for the following year. The revenue target is typically a function of the number of sellers, the addressable market size and key accounts in a given territory, average spend and conversion rate given prior years’ experience, etc. This straight forward math has not changed in probably decades, but it assumes that the underlying data are 100% correct. This data includes:
- Number of accounts with a decision-making location in a territory
- Related IT spend and prioritization
- Organizational characteristics like legal ownership, industry code, credit score, annual report figures, etc.
- Key contacts, roles and sentiment
- Prior interaction (campaign response, etc.) and transaction (quotes, orders, payments, products, etc.) history with the firm
Every organization, no matter if it is a life insurer, a pharmaceutical manufacturer, a fashion retailer or a construction company knows this math and plans on getting somewhere above 85% achievement of the resulting target. Office locations, support infrastructure spend, compensation and hiring plans are based on this and communicated.
So why is it that when it is an open secret that the underlying data is far from perfect (accurate, current and useful) and corrupts outcomes, too few believe that fixing it has any revenue impact? After all, we are not projecting the climate for the next hundred years here with a thousand plus variables.
If corporate hierarchies are incorrect, your spend projections based on incorrect territory targets, credit terms and discount strategy will be off. If every client touch point does not have a complete picture of cross-departmental purchases and campaign responses, your customer acquisition cost will be too high as you will contact the wrong prospects with irrelevant offers. If billing, tax or product codes are incorrect, your billing will be off. This is a classic telecommunication example worth millions every month. If your equipment location and configuration is wrong, maintenance schedules will be incorrect and every hour of production interruption will cost an industrial manufacturer of wood pellets or oil millions.
Also, if industry leaders enjoy an upsell ratio of 17%, and you experience 3%, data (assuming you have no formal upsell policy as it violates your independent middleman relationship) data will have a lot to do with it.
The challenge is not the fact that data can create revenue improvements but how much given the other factors: people and process.
Every industry laggard can identify a few FTEs who spend 25% of their time putting one-off data repositories together for some compliance, M&A customer or marketing analytics. Organic revenue growth from net-new or previously unrealized revenue is what the focus of any data management initiative should be. Don’t get me wrong; purposeful recruitment (people), comp plans and training (processes) are important as well. Few people doubt that people and process drives revenue growth. However, few believe data being fed into these processes has an impact.
This is a head scratcher for me. An IT manager at a US upstream oil firm once told me that it would be ludicrous to think data has a revenue impact. They just fixed data because it is important so his consumers would know where all the wells are and which ones made a good profit. Isn’t that assuming data drives production revenue? (Rhetorical question)
A CFO at a smaller retail bank said during a call that his account managers know their clients’ needs and history. There is nothing more good data can add in terms of value. And this happened after twenty other folks at his bank including his own team delivered more than ten use cases, of which three were based on revenue.
Hard cost (materials and FTE) reduction is easy, cost avoidance a leap of faith to a degree but revenue is not any less concrete; otherwise, why not just throw the dice and see how the revenue will look like next year without a central customer database? Let every department have each account executive get their own data, structure it the way they want and put it on paper and make hard copies for distribution to HQ. This is not about paper versus electronic but the inability to reconcile data from many sources on paper, which is a step above electronic.
Have you ever heard of any organization move back to the Fifties and compete today? That would be a fun exercise. Thoughts, suggestions – I would be glad to hear them?