Tag Archives: Data Governance
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by the finance and accounting organization’s ability to proactively analyze that information before it is stale or out of date”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
In my last blog I promised I would report back my experience on using Informatica Data Quality, a software tool that helps automate the hectic, tedious data plumbing task, a task that routinely consumes more than 80% of the analyst time. Today, I am happy to share what I’ve learned in the past couple of months.
But first, let me confess something. The reason it took me so long to get here was that I was dreaded by trying the software. Never a savvy computer programmer, I was convinced that I would not be technical enough to master the tool and it would turn into a lengthy learning experience. The mental barrier dragged me down for a couple of months and I finally bit the bullet and got my hands on the software. I am happy to report that my fear was truly unnecessary – It took me one half day to get a good handle on most features in the Analyst Tool, a component of the Data Quality designed for analyst and business users, then I spent 3 days trying to figure out how to maneuver the Developer Tool, another key piece of the Data Quality offering mostly used by – you guessed it, developers and technical users. I have to admit that I am no master of the Developer Tool after 3 days of wrestling with it, but, I got the basics and more importantly, my hands-on interaction with the entire software helped me understand the logic behind the overall design, and see for myself how analyst and business user can easily collaborate with their IT counterpart within our Data Quality environment.
To break it all down, first comes to Profiling. As analyst we understand too well the importance of profiling as it provides an anatomy of the raw data we collected. In many cases, it is a must have first step in data preparation (especially when our raw data came from different places and can also carry different formats). A heavy user of Excel, I used to rely on all the tricks available in the spreadsheet to gain visibility of my data. I would filter, sort, build pivot table, make charts to learn what’s in my raw data. Depending on how many columns in my data set, it could take hours, sometimes days just to figure out whether the data I received was any good at all, and how good it was.
Switching to the Analyst Tool in Data Quality, learning my raw data becomes a task of a few clicks – maximum 6 if I am picky about how I want it to be done. Basically I load my data, click on a couple of options, and let the software do the rest. A few seconds later I am able to visualize the statistics of the data fields I choose to examine, I can also measure the quality of the raw data by using Scorecard feature in the software. No more fiddling with spreadsheet and staring at busy rows and columns. Take a look at the above screenshots and let me know your preference?
Once I decide that my raw data is adequate enough to use after the profiling, I still need to clean up the nonsense in it before performing any analysis work, otherwise bad things can happen — we call it garbage in garbage out. Again, to clean and standardize my data, Excel came to rescue in the past. I would play with different functions and learn new ones, write macro or simply do it by hand. It was tedious but worked if I worked on static data set. Problem however, was when I needed to incorporate new data sources in a different format, many of the previously built formula would break loose and become inapplicable. I would have to start all over again. Spreadsheet tricks simply don’t scale in those situation.
With Data Quality Analyst Tool, I can use the Rule Builder to create a set of logical rules in hierarchical manner based on my objectives, and test those rules to see the immediate results. The nice thing is, those rules are not subject to data format, location, or size, so I can reuse them when the new data comes in. Profiling can be done at any time so I can re-examine my data after applying the rules, as many times as I like. Once I am satisfied with the rules, they will be passed on to my peers in IT so they can create executable rules based on the logic I create and run them automatically in production. No more worrying about the difference in format, volume or other discrepancies in the data sets, all the complexity is taken care of by the software, and all I need to do is to build meaningful rules to transform the data to the appropriate condition so I can have good quality data to work with for my analysis. Best part? I can do all of the above without hassling my IT – feeling empowered is awesome!
Use the right tool for the right job will improve our results, save us time, and make our jobs much more enjoyable. For me, no more Excel for data cleansing after trying our Data Quality software, because now I can get a more done in less time, and I am no longer stressed out by the lengthy process.
I encourage my analyst friends to try Informatica Data Quality, or at least the Analyst Tool in it. If you are like me, feeling weary about the steep learning curve then fear no more. Besides, if Data Quality can cut down your data cleansing time by half (mind you our customers have reported higher numbers), how many more predictive models you can build, how much you will learn, and how much faster you can build your reports in Tableau, with more confidence?
That second question is a killer because most people — no matter if they’re in marketing, sales or manufacturing — rely on incomplete, inaccurate or just plain wrong information. Regardless of industry, we’ve been fixated on historic transactions because that’s what our systems are designed to provide us.
“Moneyball: The Art of Winning an Unfair Game” gives a great example of what I mean. The book (not the movie) describes Billy Beane hiring MBAs to map out the factors that would win a baseball game. They discovered something completely unexpected: That getting more batters on base would tire out pitchers. It didn’t matter if batters had multi-base hits, and it didn’t even matter if they walked. What mattered was forcing pitchers to throw ball after ball as they faced an unrelenting string of batters. Beane stopped looking at RBIs, ERAs and even home runs, and started hiring batters who consistently reached first base. To me, the book illustrates that the most useful knowledge isn’t always what we’ve been programmed to depend on or what is delivered to us via one app or another.
For years, people across industries have turned to ERP, CRM and web analytics systems to forecast sales and acquire new customers. By their nature, such systems are transactional, forcing us to rely on history as the best predictor of the future. Sure it might be helpful for retailers to identify last year’s biggest customers, but that doesn’t tell them whose blogs, posts or Tweets influenced additional sales. Isn’t it time for all businesses, regardless of industry, to adopt a different point of view — one that we at Informatica call “Data-First”? Instead of relying solely on transactions, a data-first POV shines a light on interactions. It’s like having a high knowledge IQ about relationships and connections that matter.
A data-first POV changes everything. With it, companies can unleash the killer app, the killer sales organization and the killer marketing campaign. Imagine, for example, if a sales person meeting a new customer knew that person’s concerns, interests and business connections ahead of time? Couldn’t that knowledge — gleaned from Tweets, blogs, LinkedIn connections, online posts and transactional data — provide a window into the problems the prospect wants to solve?
That’s the premise of two startups I know about, and it illustrates how a data-first POV can fuel innovation for developers and their customers. Today, we’re awash in data-fueled things that are somehow attached to the Internet. Our cars, phones, thermostats and even our wristbands are generating and gleaning data in new and exciting ways. That’s knowledge begging to be put to good use. The winners will be the ones who figure out that knowledge truly is power, and wield that power to their advantage.
But it’s not as easy as a couple of queries. The reality is that the body of knowledge in question is seldom in a shape recognizable as a ‘body’. In most corporations, the data regulators are asking for is distributed throughout the organization. Perhaps a ‘Scattering of Knowledge’ is a more appropriate metaphor.
It is time to accept that data distribution is here to stay. The idea of a single ERP has long gone. Hype around Big Data is dying down, and being replaced by a focus on all data as a valuable asset. IT architectures are becoming more complex as additional data storage and data fueled applications are introduced. In fact, the rise of Data Governance’s profile within large organizations is testament to the acceptance of data distribution, and the need to manage it. Forrester has just released their first Forrester Wave ™ on data governance. They state it is time to address governance as “Data-driven opportunities for competitive advantage abound. As a consequence, the importance of data governance — and the need for tooling to facilitate data governance —is rising.” (Informatica is recognized as a Leader)
However, Data Governance Programs are not yet as widespread as they should be. Unfortunately it is hard to directly link strong Data Governance to business value. This means trouble getting a senior exec to sponsor the investment and cultural change required for strong governance. Which brings me back to the opportunity within Regulatory Compliance. My thinking goes like this:
- Regulatory compliance is often about gathering and submitting high quality data
- This is hard as the data is distributed, and the quality may be questionable
- Tools are required to gather, cleanse, manage and submit data for compliance
- There is a high overlap of tools & processes for Data Governance and Regulatory Compliance
So – why not use Regulatory Compliance as an opportunity to pilot Data Governance tools, process and practice?
Far too often compliance is a once-off effort with a specific tool. This tool collects data from disparate sources, with unknown data quality. The underlying data processes are not addressed. Strong Governance will have a positive effect on compliance – continually increasing data access and quality, and hence reducing the cost and effort of compliance. Since the cost of non-compliance is often measured in millions, getting exec sponsorship for a compliance-based pilot may be easier than for a broader Data Governance project. Once implemented, lessons learned and benefits realized can be leveraged to expand Data Governance into other areas.
Previously I likened Regulatory Compliance as a Buy One, Get One Free opportunity: Compliance + a free performance boost. If you use your compliance budget to pilot Data Governance – the boost will be larger than simply implementing Data Quality and MDM tools. The business case shouldn’t be too hard to build. Consider that EY’s research shows that companies that successfully use data are already outperforming their peers by as much as 20%.[i]
Data Governance Benefit = (Cost of non-compliance + 20% performance boost) – compliance budget
Yes, the equation can be considered simplistic. But it is compelling.
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
The other comparison is that data is like solar power. Like solar power, data is abundant. In addition, it’s getting cheaper and more efficient to harness. The juxtaposition of these images captures the current sentiment around data’s potential to improve our lives in many ways. For this to happen, however, corporations and data custodians must effectively balance the power of data with security and privacy concerns.
Many people have a preconception of security as an obstacle to productivity. Actually, good security practitioners understand that the purpose of security is to support the goals of the company by allowing the business to innovate and operate more quickly and effectively. Think back to the early days of online transactions; many people were not comfortable banking online or making web purchases for fear of fraud and theft. Similar fears slowed early adoption of mobile phone banking and purchasing applications. But security ecosystems evolved, concerns were addressed, and now Gartner estimates that worldwide mobile payment transaction values surpass $235B in 2013. An astute security executive once pointed out why cars have brakes: not to slow us down, but to allow us to drive faster, safely.
The pace of digital change and the current proliferation of data is not a simple linear function – it’s growing exponentially – and it’s not going to slow down. I believe this is generally a good thing. Our ability to harness data is how we will better understand our world. It’s how we will address challenges with critical resources such as energy and water. And it’s how we will innovate in research areas such as medicine and healthcare. And so, as a relatively new Informatica employee coming from a security background, I’m now at a crossroads of sorts. While Informatica’s goal of “Putting potential to work” resonates with my views and helps customers deliver on the promise of this data growth, I know we need to have proper controls in place. I’m proud to be part of a team building a new intelligent, context-aware approach to data security (Secure@SourceTM).
We recently announced Secure@SourceTM during InformaticaWorld 2014. One thing that impressed me was how quickly attendees (many of whom have little security background) understood how they could leverage data context to improve security controls, privacy, and data governance for their organizations. You can find a great introduction summary of Secure@SourceTM here.
I will be sharing more on Secure@SourceTM and data security in general, and would love to get your feedback. If you are an Informatica customer and would like to help shape the product direction, we are recruiting a select group of charter customers to drive and provide feedback for the first release. Customers who are interested in being a charter customer should register and send email to SecureCustomers@informatica.com.
There is no shortage of buzzwords that speak to the upside and downside of data. Big Data, Data as an Asset, the Internet of Things, Cloud Computing, One Version of the Truth, Data Breach, Black Hat Hacking, and so on. Clearly we are in the Information Age as described by Alvin Toffler in The Third Wave. But yet, most organizations are not effectively dealing with the risks of a data-driven economy nor are they getting the full benefits of all that data. They are stuck in a fire-fighting mode where each information management opportunity or problem is a one-time event that is man-handled with heroic efforts. There is no repeatability. The organization doesn’t learn from prior lessons and each business unit re-invents similar solutions. IT projects are typically late, over budget, and under delivered. There is a way to break out of this rut. (more…)