|By Alberto Pan||
|December 18, 2016 04:00 PM EST||
How Data Virtualization Helps Build Self-Reliance for Information Self-Service
Information self-service is undoubtedly one of the main drivers of Modern Data Management. From "data services marketplaces" to "self-service Big Data analytics," one of the objectives of most data-related initiatives today is to provide business professionals with new ways to solve their information needs with the goals of achieving self-reliance and minimizing the IT bottleneck. However, is it realistic to expect business users to assume this job?
Studies  report that more than 60 percent of companies grade their experience with self-service initiatives as "average" or lower, with nearly four out of five (73 percent) claiming that "...it requires more training than expected." So, what is the problem and what can we do to solve it? Let's start with the easy part: data visualization, which is the last stage of the data analysis process. Self-service BI tools have been around for some years now, allowing business data analysts to create their own graphical reports. Although those tools are not for every business user, business analysts with data experience, basic knowledge of statistics and a bit of SQL, can use them successfully.
The problem is that these tools are only effective when users work on previously curated/integrated datasets, such as the ones we typically find in data marts and other tightly controlled data repositories. In those environments, the source data has been carefully transformed and integrated so it can be presented in a business-friendly form. Table names correspond with business entities, column names and data formats follow business conventions, and all the data has been centralized in the same place.
Even the new breed of data preparation tools has similar problems as self-service BI tools. They enable data analysts to perform some simple data transformations as a previous step to visualization, but they inherently assume that all data has been previously moved to the same repository and that the datasets are understandable for the user.
The problem with this, of course, is that only IT can create such curated, integrated repositories. While data analysts have some technical knowledge, they cannot be expected to understand the technical details of each source system, which have different data representation models (relational, NoSQL, HDFS, multidimensional, etc.), different query languages, and are suited for different types of queries. Neither can they be expected to understand technical naming conventions (e.g., SAP technical names) or specify complex connection details (e.g., Kerberos settings). In addition, their basic SQL knowledge is far from sufficient to write the complex queries and transformations required to obtain data from the original sources in business-friendly form. Moreover, in most cases the data for a report is distributed across several systems, which further complicates things.
Where does this leave self-service? As soon as the user deviates a bit from what the designers of the curated repository had in mind, we are back to the same old process. IT needs to get involved, with the added problem that now they have more users to worry about. It's no surprise that the above-mentioned report also states that among the main problems it can "...spawn more requests to IT than before."
Data virtualization (DV) provides a way out from this apparent dead end by allowing separation of IT-related integration concerns from the business-related integration ones. The key point is that it allows IT to easily create reusable, virtual data views that expose the data in business-friendly form. These virtual datasets appear to the analyst as if they came from a single system with a consistent data representation model and query language. They use business terminology instead of technical terminology, and hide the complex transformations that are needed to present data in the way in which business users are familiar. When they don't exist in the data sources, data virtualization allows the user to add metadata to these virtual views, such as adding business-friendly column descriptions and specifying the relationships between different datasets.
It's also easy to expose different logical views over the same physical data, adapted to the needs of different business units and at different levels of granularity. Because data virtualization requires no data replication, this process is much faster than using traditional data integration strategies. Without data virtualization, new physical repositories for each business unit and each desired data granularity would be required, a process well known to be slow, costly, and prone to data inconsistencies. In turn, creating new logical views with data virtualization can be made at almost negligible cost.
Finally, and with this approach, data analysts are abstracted from the changes in the underlying infrastructure. For instance, they will not notice if now the data for a report originates from Hadoop instead of the data warehouse.
To create these business-friendly views, IT manages the details of connecting to data sources, defining data transformations, and combining information from several data sources. Unlike self-service BI and data preparation tools, data virtualization tools apply sophisticated distributed query execution techniques to ensure optimal performance even when dealing with very large data sets distributed across several data sources (see my posts at datavirtualizationblog.com for details).
Data virtualization also provides IT with a single, entry point for monitoring usage, and for creating virtual "trust domains" to apply different security and governance rules for different types of users. For example, data analysts performing an exploratory job for their own use can benefit from "light" governance rules, while shared reports go through a "hard" governance process. Notice that governance is key to avoid another of the key problems mentioned in the above report: "report chaos", which consists in different business users having supposedly similar reports which, in practice, offer different results.
Data virtualization also provides a single-entry point to establish workload management policies to set limits in resource usage. For example, IT may want to set limits on the number of requests that a certain business unit can make against a certain data source.
Data virtualization not only helps IT, it also makes it easier for data analysts to search and discover data. Best-of-breed DV tools allow browsing of all datasets and their relationships, and provide a Google-like interface to search both data and metadata across all the data sets.
The capability of creating different logical views over the same data is also crucial for enabling iterative processes. Using data virtualization, the cost of iterating over several versions of a report is very low. This also changes the interaction with IT when a new component gains enough usage to be worth "operationalizing," or when we need a new business-friendly view to be added. In this instance, instead of sending a written request, the analyst can share a prototype of the desired new component.
In summary, data virtualization can act as the bridge between IT and business by providing a common language for both groups, enabling an effective division of work where each group focuses on its core functions, and making it easier for them to collaborate. DV also allows IT to monitor and govern the process, avoid chaos and provide help when needed.
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