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The need for businesses to process and analyze data has grown in intensity along with the volumes of data they are amassing. Our benchmark research consistently shows that preparing data is the most widespread impediment to analytic and operational efficiency. In our recent research on data and analytics in the cloud, more than half (55%) of organizations said that preparing data for analysis is a major impediment, followed by other preparatory tasks: reviewing data for quality and consistency (48%) and waiting for data and information (28%). Organizations that want to apply analytics to make more effective decisions and take prompt actions need to find ways to shorten the work that comes before it. Conventional analytics and business intelligence tools are not designed for data preparation, but new software tools can enable business users independently or in concert with IT to perform the tasks needed.
Datawatch offers one such tool, which I have described as doing advanced information optimization. The company has helped organizations get more out of their information assets for years. One could argue that Datawatch has been doing data preparation longer than anyone else in the market; its Monarch product dates back to the 1990s and specializes in extracting and manipulating data from a range of documents and systems. The latest release, version 13, takes significant steps forward with a new user environment and functionality designed for analysts and users in operational positions who need a more intuitive approach to data preparation. Its drag-and-drop approach helps make data preparation doable for most business users.
Datawatch has continued to expand its data access support for various systems including salesforce.com and Hadoop for big data, providing support for both cloud-based and on-premises systems. It has significant support already for the PDF, JSON, and XML standards, as well as text, invoices, documents and log files. It also enables getting data from and putting data back into Microsoft Excel spreadsheets. Datawatch takes an “inspect and recommend” approach to data preparation, which can be especially useful with unstructured data in documents and files. Monarch can identify the data’s format and structure, present what can be extracted and enable setup of a template to use with similar data. A capability the company calls reliable redaction helps in sharing information; after data is extracted it applies masking for privacy purposes to ensure that no regulations and compliance policies are violated. Monarch also supports dataset preparation with many analytic tools like Tableau in which users struggle to access, blend, enrich and use data. Datawatch also has a visual analytics tool it acquired that complements its data preparation.
As organizations examine Datawatch’s tool, they should be aware that it is for more than one-off data preparation, providing a repeatable approach that can save time not only for individuals but for entire teams. Datawatch enables many individuals to share data and collaborate on work. This helps analysts and operations professionals work together, as well as involving IT in complex data-related processes.
Datawatch continues to grow its customer base in data preparation. A prominent example is Marbridge Foundation, which we recognized with our 2015 Leadership Award in Information Optimization for its work to make content and data from its systems comply with the Affordable Care Act – a project driven by its CFO. Marbridge took advantage of the flexibility in Datawatch’s software to extract and process data from Adobe Acrobat files in PDF format along with other applications to gain confidence that it is in compliance with this critical legislation. Data preparation provides the most value in lines of business such as finance, human resources, operations, marketing, sales, operations, customer and supply chain. Having a tool for data preparation that connects to business processes can produce significant impacts.
Data preparation software serves a growing market of business users who need to handle data but are not adept enough to do it with general analytics and business intelligence tools or to use data integration tools intended for IT professionals. Software providers that still use administrative or other IT-centric interfaces fall short of offering an independent and universal approach to data preparation. In addition to supporting conventional data sources and systems Datawatch handles content and documents to help establish a consistent approach to preparing all data needed in business with one tool.
With its heritage in data preparation Datawatch is well positioned to be on the short list of tools that help organizations realize the full value of information assets from inside or outside of the business. We recommend that organizations consider Datawatch and its advances in Monarch 13 for meeting a broader set of data preparation needs.
CEO and Chief Research Officer
Data is an essential ingredient for every aspect of business, and those that use it well are likely to gain advantages over competitors that do not. Our benchmark research on information optimization reveals a variety of drivers for deploying information, most commonly analytics, information access, decision-making, process improvements and customer experience and satisfaction. To accomplish any of these purposes requires that data be prepared through a sequence of steps: accessing, searching, aggregating, enriching, transforming and cleaning data from different sources to create a single uniform data set. To prepare data properly, businesses need flexible tools that enable them to enrich the context of data drawn from multiple sources, collaborate on its preparation to serve business needs and govern the process of preparation to ensure security and consistency. Users of these tools range from analysts to operations professionals in the lines of business.
Data preparation efforts often encounter challenges created by the use of tools not designed for these tasks. Many of today’s analytics and business intelligence products do not provide enough flexibility, and data management tools for data integration are too complicated for analysts who need to interact ad hoc with data. Depending on IT staff to fill ad hoc requests takes far too long for the rapid pace of today’s business. Even worse, many organizations use spreadsheets because they are familiar and easy to work with. However, when it comes to data preparation, spreadsheets are awkward and time-consuming and require expertise to code them to perform these tasks. They also incur risks of errors in data and inconsistencies among disparate versions stored on individual desktops.
In effect inadequate tools waste analysts’ time, which is a scarce resource in many organizations, and can squander market opportunities through delays in preparation and unreliable data quality. Our information optimization research shows that most analysts spend the majority of their time not in actual analysis but in readying the data for analysis. More than 45 percent of their time goes to preparing data for analysis or reviewing the quality and consistency of data.
Businesses need technology tools capable of handling data preparation tasks quickly and dependably so users can be sure of data quality and concentrate on the value-adding aspects of their jobs. More than a dozen such tools designed for these tasks are on the market. The best among them are easy for analysts to use, which our research shows is critical: More than half (58%) of participants said that usability is a very important evaluation criterion, more than any other, in software for optimizing information. These tools also deal with the large numbers and types of sources organizations have accumulated: 92 percent of those in our research have 16 to 20 data sources, and 80 percent have more than 20 sources. Complicating the issue further, these sources are not all inside the enterprise; they also are found on the Internet and in cloud-based environments where data may be in applications or in big data stores.
Organizations can’t make business use of their data until it is ready, so simplifying and enhancing the data preparation process can make it possible for analysts to begin analysis sooner and thus be more productive. Our analysis of time related to data preparation finds that when this is done right, significant amounts of time could be shifted to tasks that contribute to achieving business goals. We conclude that, assuming analysts spend 20 hours a week working on analytics, most are spending six hours on preparing data, another six hours on reviewing data for quality and consistency issues, three more hours on assembling information, another two hours waiting for data from IT and one hour presenting information for review; this leaves only two hours for performing the analysis itself.
Dedicated data preparation tools provide support for key tasks in areas that our research and experience finds that are done manually by about one-third of organizations. These data tasks include search, aggregation, reduction, lineage tracking, metrics definition and collaboration. If an organization is able to reduce the 14 hours previously mentioned in data-related tasks (that including preparing data, reviewing data and waiting for data from IT) by one-third, it will have an extra four hours a week for analysis – that’s 10 percent of a 40-hour work week. Multiply this time by the number of individual analysts and it becomes significant. Using the proper tools can enable such a reallocation of time to use the professional expertise of these employees.
This savings can apply in any line of business. For example, our research into next-generation finance analytics shows that more than two-thirds (68%) of finance organizations spend most of their analytics time on data-related tasks. Further analysis shows that only 36 percent of finance organizations that spend the most time on data-related tasks can produce metrics within a week, compared to more than half (56%) of those that spend more time on analytic tasks. This difference is important to finance organizations seeking to take a more active role in corporate decision-making.
Another example is found in big data. The flood of business data has created even more challenges as the types of sources have expanded beyond just the RDBMS and data appliances; Hadoop, in-memory and NoSQL big data sources exist in at least 25 percent of organizations, according to our big data integration research. Our projections of growth based on what companies are planning indicates that Hadoop, in-memory and NoSQL sources will increase significantly. Each of these types must draw from systems from various providers, which have specific interfaces to access data let alone load it. Our research in big data finds similar results regarding data preparation: The tasks that consume the most time are reviewing data for quality and consistency (52%) and preparing data (46%). Without automating data preparation for accessing and streamlining the loading of data, big data can be an insurmountable task for companies seeking efficiency in their deployments.
A third example is in the critical area of customer analytics. Customer data is used across many departments but especially marketing, sales and customer service. Our research again finds similar issues regarding time lost to data preparation tasks. In our next-generation customer analytics benchmark research preparing data is the most time-consuming task (in 47% of organizations), followed closely by reviewing data (43%). The research also finds that data not being readily available is the most common point of dissatisfaction with customer analytics (in 63% of organizations). Our research finds other examples, too, in human resources, sales, manufacturing and the supply chain.
The good news is that these business-focused data preparation tools have usability in the form of spreadsheet-like interfaces and include analytic workflows that simplify and enhance data preparation. In searching for and profiling of data and examining fields based on analytics, use of color can help highlight patterns in the data. Capabilities for addressing duplicate and incorrect data about, for example, companies, addresses, products and locations are built in for simplicity of access and use. In addition data preparation is entering a new stage in which machine learning and pattern recognition, along with predictive analytics techniques, can help guide individuals to issues and focus their efforts on looking forward. Tools also are advancing in collaboration, helping teams of analysts work together to save time and take advantage of colleagues’ expertise and knowledge of the data, along with interfacing to IT and data management professionals. In our information optimization research collaboration is a critical technology innovation, according to more than half (51%) of organizations. They desire several collaborative capabilities ranging from discussion forms to knowledge sharing to requests on activity streams.
This data preparation technology provides support for ad hoc and other agile approaches to working with data that maps to how business actually operate. Taking a dedicated approach can help simplify and speed data preparation and add value by enabling users to perform analysis sooner and allocate more time to it. If you have not taken a look at how data preparation can improve analytics and operational processes, I recommend that you start now. Organizations are saving time and becoming more effective by focusing more on business value-adding tasks.
CEO and Chief Research Officer