Analytics and traditional business intelligence are experiencing a massive technological shift. One that is generating a constructive dialog around how to combine the tried and true with the new realities of exponential data growth resulting from rampant digitization.
As organizations adjust their data strategy to deal with the ever-increasing volume and variety of data, data driven organizations are building self-service capabilities into their analytics and reporting platforms to develop robust, high-value systems of insight.
Time-to-decision is becoming more critical as digital capabilities develop and fundamentally alter how companies interact with customers, supply chains and partners. Sales and marketing teams are using analytics to better understand customer buying models, sentiment patterns, and e-commerce behaviors to rapidly adapt their plans in near real-time.
Financial analysts need to be able to quickly pull planning, budget and forecast information from various operating units and blend the data in order to fully understand overall expense and cost. The pace of business in an era of digital acceleration no longer tolerates the cycles of traditional extract, transformation and loading (ETL) processes common to traditional BI architectures.
Transportation and logistics companies are altering warehouse cold storage to dramatically reduce refrigerations costs by optimizing density, improve driver productivity and vehicle tracking. The Internet of Things will unleash torrents of data as fleets get updated with sensor rich tractors and trailers. Companies like Peloton and Ot.to (just acquired by Uber) are looking to make trucking a much more data driven affair.
All of these use cases create data-rich environments to apply analytics against.
Modernizing BI doesn't have to be a zero-sum game, however. The current genre of data preparation tools empower data professionals to cleanse, shape and blend myriad data sources at scale well beyond the limits of the ubiquitous spreadsheet while keeping with a familiar user experience. Combine these capabilities with the current generation of data visualization tools, machine learning platforms and elastic storage and compute frameworks to complement the existing reporting, business rules, metadata, and governance assets that are in place.
Folding in self-service capabilities adds tremendous value to organizations - well beyond the initial investment, regardless of industry. The synergy is amplified when self-service data preparation and advanced visualization come together. When data professionals (analysts, data scientists, and builders of data products) are freed from the repetitive tedium of cleansing, blending, shaping and generally preparing data for analysis, they can focus on what matters - converting analysis and insight into action that drives business outcomes.
Data Access and Preparation Challenges
The biggest untapped source of analytical value within most firms data assets resides in formats that aren't structured in traditional analytics ready format. These could be multi, semi-structured or unstructured documents like log files, text reports, web pages, JSON files and such. As individuals toil to unlock this value, they spend countless hours fiddling with spreadsheets, re-keying and reconciling the data just to get it into an analytics ready format. Aside from being time-intensive and error prone, it becomes a process unto itself and results in 'shadow BI' which can run afoul of governance and compliance objectives.
Data preparation platforms solve many of these challenges by empowering business users, data scientists and analysts to easily acquire, clean, manipulate and blend data from virtually any source to create data sets that are ideally suited for visual discovery using tools like Tableau or cognitive solutions such as IBM Watson Analytics (and yes, Excel too).
Preparing data for analysis at scale in a fraction of the time through machine learning algorithms, annotated workflow automation and full traceability are some of the hallmarks in today's leading data preparation solutions.
Liberating analysis from some of the burden that comes with 'making-do' data preparation activities results in timely, more informed business decisions along with improved operational processes and governance.
Self-service Data Preparation: creating a win-win for the Business and IT
Bridging the gap between ease of use and agility required by today's digital business and information technology best practices of data protection and security, scalability, automation, and governance is a critical success factor.
Unfortunately, data visualization and analytic solutions can come up short because of data retrieval and preparation challenges. Current data prep tools can fill this void, which in turn promotes the creation of data products that reinforce a culture of data driven decision making.
A Virtuous and Powerful Combination
Together, self-service analytics and data preparation empower ordinary users to do extraordinary things with their data, which in turn helps foster a culture of data-driven decision making that delivers business value.
Remove friction from the data supply chain with self service capabilities while satisfying information technology objectives - find the right balance of freedom and responsibility with a modern BI architecture.