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Using information technology to make data useful is as old as the Information Age. The difference today is that the volume and variety of  available data has grown enormously. Big data gets almost all of the attention, but there’s also cryptic data. Both are difficult to harness using basic tools and require new technology to help organizations glean actionable information from the large and chaotic mass of data. “Big data” refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially those related to human behavior and interaction. The challenges in dealing with big data include having the computational power that can scale to the processing requirements for the volumes involved; analytical tools to work with the large data sets; and governance necessary to manage the large data sets to ensure that the results of the analysis are accurate and meaningful. But that’s not all organizations have to deal with now. I’ve coined the term “cryptic data” to focus on a different, less well known sort of data challenge that many companies and individuals face.

Cryptic data sets aren’t easy to find or aren’t easily accessed by people who could make use of them. Why “cryptic?” As a scuba diver, I donate time to Reef Check by doing scientific species counts in and around Monterey Bay, Calif. Cryptic organisms are ones that hide out deep in the cracks and crevices of our rocky reefs. Finding and counting them accurately is time-consuming and requires skill. Similarly, it’s difficult to locate, access and collect cryptic data routinely. Because it’s difficult to locate or access routinely, those who have it can gain a competitive advantage over those who don’t. The main reason cryptic data is largely untapped is cost vs. benefits: The time, effort, money and other resources required to manually retrieve it and get it into usable form may be greater than the value of having that information.

By automating the process of routinely collecting information and transforming it into a usable form and format, technology can expand the range of data available by lowering the cost side of the equation. So far, most tools, such as Web crawlers, have been designed to be used by IT professionals. Data integration software, also mainly used by IT departments, helps transform the data collected into a form and format where it can be used by analysts to create mashups or build data tables for analysis to support operational processes. Data integration tools mainly work with internal, structured data and a majority have little or no capability to support data acquisition in the Web. Tools designed for IT professionals are a constraint in making better use of cryptic data because business users are subject matter experts. They have a better idea of the information they need and are in a better position to understand the subtleties and ambiguities in the information they collect. To address this constraint, Web scraping tools (what I call “data drones”) have appeared that are designed for business users. They use a more visual user interface design and hide some of the complexity inherent in the process. They can automate the process of collecting cryptic data and expand the scope and depth of data used for analysis, alerting and decision support.

Cryptic data can be valuable because when collected, aggregated and analyzed, it provides companies and individuals with information and insight that were unavailable. This is particularly true of data sets gathered over time from a source or combination of sources that can reveal trends and relationships that otherwise would be difficult to spot.

Cryptic data can exist within a company’s firewall (typically held in desktop spreadsheets or other files maintained by an individual as well as in “dark” operational data sets), but usually it is somewhere in the Internet cloud. For example, it may be

  • Industry data collected by some group that is only available to members
  • A composite list of products from gathered from competitors’ websites
  • Data contained in footnotes in financial filings that are not collected in tabular form by data aggregators
  • Tables of related data assembled through repetitive queries of a free or paid data source (such as patents, real estate ownership or uniform commercial code filings).

Along these lines, our next-generation finance analytics benchmark research shows that companies have limited access to information about markets, industries and- economies.vr_NG_Finance_Analytics_17_accessibility_of_external_dataOnly 14 percent of participants said they have access all the external data they need. Most (63%) said they can access only some of it, and another 14 percent said they can’t access any such data. In the past, this lack of access was even more common, but the Internet changed that. And this type of external data is worth going after, as it can help organizations build better models, perform deeper analysis or do better in assessing performance, forecasting or gauging threats and opportunities.

Cryptic data poses a different set of challenges than big data. Making big data usable requires the ability to manage large volumes of data. This includes processing large volumes, transforming data sets into usable forms, filtering extraneous data and code data for relevance or reliability, to name some of more common tasks. To be useful big data also requires powerful analytic tools that handle masses of structured and unstructured data and the talent to understand it. By contrast, the challenge of cryptic data lies in identifying and locating useful sources of information and having the ability to collect it efficiently. Both pose difficulties. Whereas making big data useful requires boiling the ocean of data, cryptic data involves collecting samples from widely distributed ponds of data. In the case of cryptic data, automating data collection makes it feasible to assemble a mosaic of data points that improves situational awareness.

Big data typically uses data scientists to tease out meaning from the masses of data (although analytics software vendors have been working on making this process simpler for business users). Cryptic data analysis is built on individual experience and insight. Often, the starting point is a straightforward hypothesis or a question in the mind of a business user. It can stem from the need to periodically access the same pools of data to better understand the current state of markets, competitors, suppliers or customers. Subject matter expertise, an analytical mind and a researcher’s experience are necessary starting capabilities for those analyzing cryptic data. These skills facilitate knowing what data to look for, how to look for it and where to look for it. Although these qualities are essential, they not sufficient. Automating the process of retrieving data from sources in a reliable fashion is a must because, as noted above, the time and expense required to acquire the data manually are greater than its value to the individual or organization.

Almost from the dawn of the Internet, Web robots (or crawlers) have been used to automate the collection of information from Web pages. Search engines, for example, use them to index Internet pages while spammers use them to collect email addresses. These robots are designed and managed by information technology professionals. Automating the process of collecting cryptic data requires software that business people can use. To make accessing cryptic data feasible, they need “data drones” that can be programmed by users with limited training to fetch information from specific Web pages. Tools available from Astera ReportMinerConnotate, Datawatch, import.io, Kofax Kapow and Mozenda are great examples on where you can get started for leveraging cryptic data. I recommend that everyone who has to routinely collect information from Internet sites or from internal data stores that are hard to access or who thinks that they could benefit from using cryptic data investigate tools available for collecting it.

Regards,

Robert Kugel – SVP Research

VentanaResearchBenchmark_FinanceAnalyticsAnalytics has long been a core discipline of Finance, applied to analysis of balance sheets, income statements and cash-flow statements. However, as I’ve noted, most finance departments have not kept up with recent advances. Our recent research in finance analytics shows that few organizations are realizing the potential of more advanced analytic methods and tools such as predictive analytics and driver-based modeling. One reason for this sluggishness is that they have not looked past yesterday’s requirements to see what possible. Another is that they are distracted by the difficulties they face in simply doing tried-and-true analysis, which is the result of difficulties in accessing the necessary data and inadequate tools. A third reason is that people receive too little training in the application of analytics to business and the use of more advanced analytic tools and methods.

Companies typically do not provide training – or testing – to enhance the analytical skills of their employees. Testing skills is useful in exposing individual and organizational gaps and injects motivation into the training process. Creating analytics requires a thorough knowledge of the basics: which methods to use under which circumstances and the best approaches to take. Newer analytic approaches and tools can be complex, so it makes sense to train people to gain a better understanding of both the basic uses of analytic tools and advanced analytics. Yet the research shows that fewer than one-third (31%) of companies provide training for people who create analytics. This may be because organizations hire people who they assume already have the skills or the talent to pick them up as needed on the job. They also may be reluctant to spend money and unaware of the indirect expenses of not doing so. Because they rarely test employee skills, organizations may not be aware of the shortfalls in competence that are costing them time and money. For whatever reason, the lack of training – especially in the creation of analytics – is shortsighted.

vr_NG_Finance_Analytics_13_training_improves_analytics_skillsTraining is vital to building analysts’ skills, especially for creating analytics. The research demonstrates a correlation. We found that a large majority (83%) of the companies that provide training in creating analytics have staffs whose skills they deem excellent or above average, compared to only half (52%) of those that provide training only in using analytics and just one-third of those that provide no training at all.

Companies that fail to test individual skills are unlikely to discover the link between weak skills and poor performance. People may complete their assignments but take twice as long as necessary to do them. If the analysts are using spreadsheets, they may be constructing models and analyses that have unseen errors (as they did in the infamous London Whale episode) or needlessly complex models because they are unfamiliar with built-in functions that simplify tasks.

Training those who create analytics also helps ensure that time spent on analytics is used as effectively as possible. In about half (47%) of the companies that train analytics creators, people spend the largest amount of their time where they should – in working with the analytics to get answers or model business issues and analysis – compared to 27 percent of organizations that only provide training for users and 20 percent that provide no training. In about three-fourths of companies that provide only training for users (73%) or no training (76%), they spend the largest amount of time on data-related tasks (waiting for data, preparing it and reviewing it for errors) rather than on analysis. Fully half of companies that provide training for creators also use analytics and performance indicators to improve individual or business unit performance, many more than those that train only users (20%) or provide no training (17%).

Analytics can be a valuable tool for finance departments and can help them play more significant roles in their organizations, which many finance professionals now want to do. But until more of them make the often extensive changes and investments the research shows they need, this goal will be more of a hope than a promise. Training finance professionals in the creation and use of both basic and advanced analytics is essential for companies to improve their performance. Being prepared and training is the best path to be effective in finance analytics.

Regards,

Robert Kugel – SVP Research

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