Category Archives: Governance, Risk and Compliance
Recently, I had the opportunity to talk to a number of CFOs about their technology priorities. These discussions represent an opportunity for CIOs to hear what their most critical stakeholder considers important. The CFOs did not hesitate or need to think much about this question. They said three things make their priority list. They are better financial system reliability, better application integration, and better data security and governance. The top two match well with a recent KPMG study which found the biggest improvement finance executives want to see—cited by 91% of survey respondents—is in the quality of financial and performance insight obtained from the data they produce, followed closely by the finance and accounting organization’s ability to proactively analyze that information before it is stale or out of date”
CFOs want to know that their systems work and are reliable. They want the data collected from their systems to be analyzed in a timely fashion. Importantly, CFOs say they are worried not only about the timeliness of accounting and financial data. This is because they increasingly need to manage upward with information. For this reason, they want timely, accurate information produced for financial and business decision makers. Their goal is to drive out better enterprise decision making.
In manufacturing, for example, CFOs say they want data to span from the manufacturing systems to the distribution system. They want to be able to push a button and get a report. These CFOs complain today about the need to manually massage and integrate data from system after system before they get what they and their business decision makers want and need.
CFOs really feel the pain of systems not talking to each other. CFOs know firsthand that they have “disparate systems” and that too much manual integration is going on. For them, they see firsthand the difficulties in connecting data from the frontend to backend systems. They personally feel the large number of manual steps required to pull data. They want their consolidation of account information to be less manual and to be more timely. One CFO said that “he wants the integration of the right systems to provide the right information to be done so they have the right information to manage and make decisions at the right time”.
Data Security and Governance
CFOs, at the same time, say they have become more worried about data security and governance. Even though CFOs believe that security is the job of the CIO and their CISO, they have an important role to play in data governance. CFOs say they are really worried about getting hacked. One CFO told me that he needs to know that systems are always working properly. Security of data matters today to CFOs for two reasons. First, data has a clear material impact. Just take a look at the out of pocket and revenue losses coming from the breach at Target. Second, CFOs, which were already being audited for technology and system compliance, feel that their audit firms will be obligated to extend what they were doing in security and governance and go as a part of regular compliance audits. One CFO put it this way. “This is a whole new direction for us. Target scared a lot of folks and will be to many respects a watershed event for CFOs”.
So the message here is that CFOs prioritize three technology objectives for their CIOs– better IT reliability, better application integration, and improved data security and governance. Each of these represents an opportunity to make the CFOs life easier but more important to enable them to take on a more strategic role. The CFOs, that we talked to, want to become one of the top three decision makers in the enterprise. Fixing these things for CFOs will enable CIOs to build a closer CFO and business relationships.
Solution Brief: The Intelligent Data Platform
Solution Brief: Secure at Source
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.
According to the Financial Executives Institute, CFOs say their second highest priority this year is to harness business intelligence and big data. Their highest priority is to improve cash flow and working capital efficiency and effectiveness. This means CFOs highest two priorities are centered around data. At roughly the same time, KPMG has found in their survey of CFOs that 91% want to improve the quality of their financial and performance insight obtained from the data that they produce. Even more amazing 51% of CFO admitted that “collecting, storing, and retrieving financial and performance data at their company is primarily accomplished through a manual and/or spreadsheet-based exercise”. From our interviews of CFOs, we believe this number is much higher.
Your question at this point—if you are not a CFO—should be how can this be the case? After all strategy consultants like Booz and Company, actively measure the degree of digitization and automation taking place in businesses by industry and these numbers year after year have shown a strong upward bias. How can the finance organization be digitized for data collection but still largely manual in its processes for putting together the figures that management and the market needs?
CFOs do not trust their data
In our interviews of CFOs, one CFO answered this question bluntly by saying “If the systems suck, then you cannot trust the numbers when you get them.” And this reality truly limits CFOs in how they respond to their top priorities. Things like management of the P&L, Expense Management, Compliance, and Regulatory all are impacted by the CFOs data problem. Instead of doing a better job at these issues, CFOs and their teams remain largely focused on “getting the numbers right”. And even worse, the answering of business questions like how much revenue is this customer providing or how profitable this customer is, involves manual pulls of data today from more than one system. And yes, similar data issues exist in financial services organizations which close the books nightly.
The CFOs, that I have talked to, admit without hesitation that data is a big issue for them. These CFOs say that they worry about data from the source and the ability to do meaningful financial or managerial analysis. They say they need to rely on data in order to report but as important they need it to help drive synergies across businesses. This matters because CFOs say they want to move from being just “bean counters” to being participants in the strategy of their enterprises.
To succeed, CFOs say that they need timely, accurate data. However, they are the first to discuss how disparate systems get in their way. CFOs believe that making their lives easier starts with the systems that support them. What they believe is needed is real integration and consolidation of data. One CFO said what is needed this way, “we need the integration of the right systems to provide the right information so we can manage and make decisions at the right time”. CFOs clearly want to know that the accounting systems are working and reliable. At the same time, CFOs want, for example, a holistic view of customer. When asked why this isn’t a marketing activity, they say this is business issue that CFOs need to help manage. “We want to understand the customer across business units. It is a finance objective because finance is responsible for business metrics and there are gaps in business metrics around customer. How much cross sell opportunities is the business as a whole pursuing?”
Chief Profitability Officers?
Jonathan Brynes at the MIT Sloan School confirms this viewpoint is becoming a larger trend when he suggests that CFOs need to take on the function of “Chief Profitability Officers”. With this hat, CFOs, in his view, need to determine which product lines, customers, segments, and channels are the most and the least profitable. Once again, this requires that CFOs tackle their data problem to have relevant, holistic information.
CIOs remain responsible for data delivery
CFOs believe that CIOs remain responsible for how data is delivered. CFOs, say that they need to lead in creating validated data and reports. Clearly, if data delivery remains a manual process, then the CFO will be severely limited in their ability to adequately support their new and strategic charter. Yet CFOs when asked if they see data as a competitive advantage say that “every CFO would view data done well as a competitive advantage”. Some CFOs even suggest that data is the last competitive advantage. This fits really well with the view of Davenport in “Competing on Analytics”. The question is how soon will CIOs and CFOs work together to get the finance organization out of its mess of manually massaging and consolidating financial and business data.
Solution Brief: The Intelligent Data Platform
Recently, my US-based job led me to a South African hotel room, where I watched Germany play Brazil in the World Cup. The global nature of the event was familiar to me. My work covers countries like Malaysia, Thailand, Singapore, South Africa and Costa Rica. And as I pondered the stunning score (Germany won, 7 to 1), my mind was drawn to emerging markets. What defines an emerging market? In particular, what are the data-related themes common to emerging markets? Because I work with global clients in the banking, oil and gas, telecommunications, and retail industries, I have learned a great deal about this. As a result, I wanted to share my top 5 observations about data in Emerging Markets.
1) Communication Infrastructure Matters
Many of the emerging markets, particularly in Africa, jumped from one or two generations of telco infrastructure directly into 3G and fiber within a decade. However, this truth only applies to large, cosmopolitan areas. International diversification of fiber connectivity is only starting to take shape. (For example, in Southern Africa, BRICS terrestrial fiber is coming online soon.) What does this mean for data management? First, global connectivity influences domestic last mile fiber deployment to households and businesses. This, in turn, will create additional adoption of new devices. This adoption will create critical mass for higher productivity services, such as eCommerce. As web based transactions take off, better data management practices will follow. Secondly, European and South American data centers become viable legal and performance options for African organizations. This could be a game changer for software vendors dealing in cloud services for BI, CRM, HCM, BPM and ETL.
2) Competition in Telecommunication Matters
If you compare basic wireless and broadband bundle prices between the US, the UK and South Africa, for example, the lack of true competition makes further coverage upgrades, like 4G and higher broadband bandwidths, easy to digest for operators. These upgrades make telecommuting, constant social media engagement possible. Keeping prices low, like in the UK, is the flipside achieving the same result. The worst case is high prices and low bandwidth from the last mile to global nodes. This also creates low infrastructure investment and thus, fewer consumers online for fewer hours. This is often the case in geographically vast countries (Africa, Latin America) with vast rural areas. Here, data management is an afterthought for the most part. Data is intentionally kept in application silos as these are the value creators. Hand coding is pervasive to string data together to make small moves to enhance the view of a product, location, consumer or supplier.
3) A Nation’s Judicial System Matters
If you do business in nations with a long, often British judicial tradition, chances are investment will happen. If you have such a history but it is undermined by a parallel history of graft from the highest to the lowest levels because of the importance of tribal traditions, only natural resources will save your economy. Why does it matter if one of my regional markets is “linked up” but shipping logistics are burdened by this excess cost and delay? The impact on data management is a lack of use cases supporting an enterprise-wide strategy across all territories. Why invest if profits are unpredictable or too meager? This is why small Zambia or Botswana are ahead of the largest African economy, Nigeria.
4) Expertise Location Matters
Anybody can have the most advanced vision on a data-driven, event-based architecture supporting the fanciest data movement and persistence standards. Without the skill to make the case to the business it is a lost cause unless your local culture still has IT in charge of specifying requirements, running the evaluation, selecting and implementing a new technology. It is also done for if there are no leaders who have experienced how other leading firms in the same or different sector went about it (un)successfully. Lastly, if you don’t pay for skill, your project failure risk just tripled. Duh!
5) Denial is Universal
No matter if you are an Asian oil company, a regional North American bank, a Central American National Bank or an African retail conglomerate. If finance or IT invested in any technologies prior and they saw a lack of adoption, for whatever reason, they will deny data management challenges despite other departments complaining. Moreover, if system integrators or internal client staff (mis)understand data management as fixing processes (which it is not) instead of supporting transactional integrity (which it is), clients are on the wrong track. Here, data management undeservedly becomes a philosophical battleground.
This is definitely not a complete list or super-thorough analysis but I think it covers the most crucial observations from my engagements. I would love to hear about your findings in emerging markets.
Stay tuned for part 2 of this series where I will talk about the denial and embrace of corporate data challenges as it pertains to an organization’s location.
A few weeks ago, a regional US bank asked me to perform some compliance and use case analysis around fixing their data management situation. This bank prides itself on customer service and SMB focus, while using large-bank product offerings. However, they were about a decade behind the rest of most banks in modernizing their IT infrastructure to stay operationally on top of things.
This included technologies like ESB, BPM, CRM, etc. They also were a sub-optimal user of EDW and analytics capabilities. Having said all this; there was a commitment to change things up, which is always a needed first step to any recovery program.
As I conducted my interviews across various departments (list below) it became very apparent that they were not suffering from data poverty (see prior post) but from lack of accessibility and use of data.
- Vendor Management & Risk
- Commercial and Consumer Depository products
- Credit Risk
- HR & Compensation
- Private Banking
- Customer Solutions
This lack of use occurred across the board. The natural reaction was to throw more bodies and more Band-Aid marts at the problem. Users also started to operate under the assumption that it will never get better. They just resigned themselves to mediocrity. When some new players came into the organization from various systemically critical banks, they shook things up.
Here is a list of use cases they want to tackle:
- The proposition of real-time offers based on customer events as simple as investment banking products for unusually high inflow of cash into a deposit account.
- The use of all mortgage application information to understand debt/equity ratio to make relevant offers.
- The capture of true product and customer profitability across all lines of commercial and consumer products including trust, treasury management, deposits, private banking, loans, etc.
- The agile evaluation, creation, testing and deployment of new terms on existing and products under development by shortening the product development life cycle.
- The reduction of wealth management advisors’ time to research clients and prospects.
- The reduction of unclaimed use tax, insurance premiums and leases being paid on consumables, real estate and requisitions due to the incorrect status and location of the equipment. This originated from assets no longer owned, scrapped or moved to different department, etc.
- The more efficient reconciliation between transactional systems and finance, which often uses multiple party IDs per contract change in accounts receivable, while the operating division uses one based on a contract and its addendums. An example would be vendor payment consolidation, to create a true supplier-spend; and thus, taking advantage of volume discounts.
- The proactive creation of central compliance footprint (AML, 314, Suspicious Activity, CTR, etc.) allowing for quicker turnaround and fewer audit instances from MRAs (matter requiring attention).
MONEY TO BE MADE – PEOPLE TO SEE
Adding these up came to about $31 to $49 million annually in cost savings, new revenue or increased productivity for this bank with $24 billion total assets.
So now that we know there is money to be made by fixing the data of this organization, how can we realistically roll this out in an organization with many competing IT needs?
The best way to go about this is to attach any kind of data management project to a larger, business-oriented project, like CRM or EDW. Rather than wait for these to go live without good seed data, why not feed them with better data as a key work stream within their respective project plans?
To summarize my findings I want to quote three people I interviewed. A lady, who recently had to struggle through an OCC audit told me she believes that the banks, which can remain compliant at the lowest cost will ultimately win the end game. Here she meant particularly tier 2 and 3 size organizations. A gentleman from commercial banking left this statement with me, “Knowing what I know now, I would not bank with us”. The lady from earlier also said, “We engage in spreadsheet Kung Fu”, to bring data together.
Given all this, what would you suggest? Have you worked with an organization like this? Did you encounter any similar or different use cases in financial services institutions?
The interesting thing is that many of the upstarts do not even intend to take on the market leader in the segment. Christensen cites the classic example of Digital Equipment Corporation in the 1980s, which was unable to make the transition from large, expensive enterprise systems to smaller, PC-based equipment. The PC upstarts in this case did not take on Digital directly – rather they addressed unmet needs in another part of the market.
Christensen wrote and published The Innovator’s Dilemma more than 17 years ago, but his message keeps reverberating across the business world. Lately, Jill Lapore questioned some of thinking that has evolved around disruptive innovation in a recent New Yorker article. “Disruptive innovation is a theory about why businesses fail. It’s not more than that. It doesn’t explain change. It’s not a law of nature,” she writes. Christensen responded with a rebuttal to Lapore’s thesis, noting that “disruption doesn’t happen overnight,” and that “[Disruptive innovation] is not a theory about survivability.”
There is something Lapore points out that both she and Christensen can agree on: “disruption” is being oversold and misinterpreted on a wide scale these days. Every new product that rolls out is now branded as “disruptive.” As stated above, the true essence of disruption is creating new markets where the leaders would not tread.
Data itself can potentially be a source of disruption, as data analytics and information emerge as strategic business assets. While the ability to provide data analysis at real-time speeds, or make new insights possible isn’t disruption in the Christensen sense, we are seeing the rise of new business models built around data and information that could bring new leaders to the forefront. Data analytics can either play a role in supporting this movement, or data itself may be the new product or service disrupting existing markets.
We’ve already been seeing this disruption taking place within the publishing industry, for example – companies or sites providing real-time or near real-time services such as financial updates, weather forecasts and classified advertising have displaced traditional newspapers and other media as information sources.
Employing data analytics as a tool for insights never before available within an industry sector also may be part of disruptive innovation. Tesla Motors, for example, is disruptive to the automotive industry because it manufactures entirely electric cars. But the formula to its success is its employment of massive amounts of data from its array of vehicle in-devices to assure quality and efficiency.
Likewise, data-driven disruption may be occurring in places that may have been difficult to innovate. For example, it’s long been speculated that some of the digital giants, particularly Google, are poised to enter the long-staid insurance industry. If this were to happen, Google would not enter as a typical insurance company with a new web-based spin. Rather, the company would be employing new techniques of data gathering, insight and analysis to offer an entirely new model to consumers – one based on data. As Christopher Hernaes recently related in TechCrunch, Google’s ability to collect and mine data on homes, business and autos give it a unique value proposition n the industry’s value chain.
We’re in an era in which Christensen’s mode of disruptive innovation has become a way of life. Increasingly, it appears that enterprises that are adept and recognizing and acting upon the strategic potential of data may be joining the ranks of the disruptors.
Last week I had the opportunity to attend the Gartner Security and Risk Management Summit. At this event, Gartner analysts and security industry experts meet to discuss the latest trends, advances, best practices and research in the space. At the event, I had the privilege of connecting with customers, peers and partners. I was also excited to learn about changes that are shaping the data security landscape.
Here are some of the things I learned at the event:
- Security continues to be a top CIO priority in 2014. Security is well-aligned with other trends such as big data, IoT, mobile, cloud, and collaboration. According to Gartner, the top CIO priority area is BI/analytics. Given our growing appetite for all things data and our increasing ability to mine data to increase top-line growth, this top billing makes perfect sense. The challenge is to protect the data assets that drive value for the company and ensure appropriate privacy controls.
- Mobile and data security are the top focus for 2014 spending in North America according to Gartner’s pre-conference survey. Cloud rounds out the list when considering worldwide spending results.
- Rise of the DRO (Digital Risk Officer). Fortunately, those same market trends are leading to an evolution of the CISO role to a Digital Security Officer and, longer term, a Digital Risk Officer. The DRO role will include determination of the risks and security of digital connectivity. Digital/Information Security risk is increasingly being reported as a business impact to the board.
- Information management and information security are blending. Gartner assumes that 40% of global enterprises will have aligned governance of the two programs by 2017. This is not surprising given the overlap of common objectives such as inventories, classification, usage policies, and accountability/protection.
- Security methodology is moving from a reactive approach to compliance-driven and proactive (risk-based) methodologies. There is simply too much data and too many events for analysts to monitor. Organizations need to understand their assets and their criticality. Big data analytics and context-aware security is then needed to reduce the noise and false positive rates to a manageable level. According to Gartner analyst Avivah Litan, ”By 2018, of all breaches that are detected within an enterprise, 70% will be found because they used context-aware security, up from 10% today.”
I want to close by sharing the identified Top Digital Security Trends for 2014
- Software-defined security
- Big data security analytics
- Intelligent/Context-aware security controls
- Application isolation
- Endpoint threat detection and response
- Website protection
- Adaptive access
- Securing the Internet of Things
This magic quadrant focuses on what Gartner calls Structured Data Archiving. Data Archiving is used to index, migrate, preserve and protect application data in secondary databases or flat files. These are typically located on lower-cost storage, for policy-based retention. Data Archiving makes data available in context of the originating business process or application. This is especially useful in the event of litigation or of an audit.
The Magic Quadrant calls out two use cases. These use cases are “live archiving of production applications” and “application retirement of legacy systems.” Informatica refers to both use cases, together, as “Enterprise Data Archiving.” We consider this to be a foundational component of a comprehensive Information Lifecycle Management strategy.
The application landscape is constantly evolving. For this reason, data archiving is a strategic component of a data growth management strategy. Application owners need a plan to manage data as applications are upgraded, replaced, consolidated, moved to the cloud and/or retired.
When you don’t have a plan in production, data accumulates in the business application. When this happens, performance bothers the business. In addition, data bloat bothers IT operations. When you don’t have a plan for legacy systems, applications accumulate in the data center. As a result, increasing budgets bother the CFO.
A data growth management plan must include the following:
- How to cycle through applications and retire them
- How to smartly store the application data
- How to ultimately dispose data while staying compliant
Structured data archiving and application retirement technologies help automate and streamline these tasks.
Informatica Data Archive delivers unparalleled connectivity, scalability and a broad range of innovative options (i.e. Smart Partitioning, Live Archiving, and retiring aging and legacy data to the Informatica Data Vault), and comprehensive retention management and data reporting and visualization. We believe our strengths in this space are the key ingredients for deploying a successful enterprise data archive.
For more information, read the Gartner Magic Quadrant for Structured Data Archiving and Application Retirement.
Regardless of the industry, new regulatory compliance requirements are more often than not treated like the introduction of a new tax. A few may be supportive, some will see the benefits, but most will focus on the negatives – the cost, the effort, the intrusion into private matters. There will more than likely be a lot of grumbling.
Across many industries there is currently a lot of grumbling, as new regulation seems to be springing up all over the place. Pharmaceutical companies have to deal with IDMP in Europe and UDI in the USA. This is hot on the heels of the US Sunshine Act, which is being followed in Europe by Aggregate Spend requirements. Consumer Goods companies in Europe are looking at the consequences of beefed up 1169 requirements. Financial Institutes are mulling over compliance to BCBS-239. Behind the grumbling most organisations across all verticals appear to have a similar approach to regulatory compliance. The pattern seems to go like this:
- Delay (The requirements may change)
- Scramble (They want it when? Why didn’t we get more time?)
- Code to Spec (Provide exactly what they want, and only what they want)
No wonder these requirements are seen as purely a cost and an annoyance. But it doesn’t have to be that way, and in fact, it should not. Just like I have seen a pattern in response to compliance, I see a pattern in the requirements themselves:
- The regulators want data
- Their requirements will change
- When they do change, regulators will be wanting even more data!
Now read the last 3 bullet points again, but use ‘executives’ or ‘management’ or ‘the business people’ instead of ‘regulators’. The pattern still holds true. The irony is that execs will quickly sign off on budget to meet regulatory requirements, but find it hard to see the value in “infrastructure” projects. Projects that will deliver this same data to their internal teams.
This is where the opportunity comes in. pwc’s 2013 State of Compliance Report[i] shows that over 42% of central compliance budgets are in excess of $1m. A significant figure. Efforts outside of the compliance team imply a higher actual cost. Large budgets are not surprising in multi-national companies, who often have to satisfy multiple regulators in a number of countries. As an alternate to multiple over-lapping compliance projects, what if this significant budget was repurposed to create a flexible data management platform? This approach could deliver compliance, but provide even more value internally.
Almost all internal teams are currently clamouring for additional data to drive ther newest application. Pharma and CG sales & marketing teams would love ready access to detailed product information. So would consumer and patient support staff, as well as down-stream partners. Trading desks and client managers within Financial Institutes should really have real-time access to their risk profiles guiding daily decision making. These data needs will not be going away. Why should regulators be prioritised over the people who drive your bottom line and who are guardians of your brand?
A flexible data management platform will serve everyone equally. Foundational tools for a flexible data management platform exist today including Data Quality, MDM, PIM and VIBE, Informatica’s Virtual Data Machine. Each of them play a significant role in easing of regulatory compliance, and as a bonus they deliver measureable business value in their own right. Implemented correctly, you will get enhanced data agility & visibility across the entire organisation as part of your compliance efforts. Sounds like ‘Buy one Get One Free’, or BOGOF in retail terms.
Unlike taxes, BOGOF opportunities are normally embraced with open arms. Regulatory compliance should receive a similar welcome – an opportunity to build the foundations for universal delivery of data which is safe, clean and connected. A 2011 study by The Economist found that effective regulatory compliance benefits businesses across a wide range of performance metrics[ii].
Is it time to get your free performance boost?