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Navigating the Data Maze

Leveraging data for actionable insights is an art. Here we suggest some best practices to create a pro-active insight ecosystem.

The challenges to implementing a Business Intelligence (BI) and Analytics strategy are varied and multi-faceted. The selection of BI tools to support business strategy and needs can be daunting. BI teams are continuously under pressure from stakeholders to deliver flexible, fast, and cost-effective solutions. Critical success factors for managing expectations of an organization’s analytics platform include: aligning with business strategy with well-meshed data architecture and fostering innovation.


As organizations look to increase revenue and become more operationally efficient, they need to measure the past, discover current trends, understand cause and effect, determine appropriate changes, and drive improvements. To that end, it is important to begin by assessing the current state of the organization and to understand the questions the business would like to answer. For instance, if business leaders were able to answer how changes in temperature or changes in precipitation affect sales, what would they do differently? Without being able to answer questions such as how changing a supply vendor or part on a bill of materials would impact their product quality, what decisions they could not make? Once these types of questions are uncovered, a clear analytics roadmap can be created.


Two significant challenges exist while delivering on an organization’s analytics needs – time and ever-evolving business requirements. In a typical scenario, analysts spend an enormous amount of time gathering, standardizing, grouping, segmenting and loading data from a myriad of systems to gain insights. In traditional data warehousing implementations, this challenge is tackled by BI and IT teams through data modelling and data Extract, Transform, and Load (ETL) processing. These types of solutions can alleviate the time it takes for an analyst to collect data but may miss the mark in terms of timeliness of providing answers when the lifespan of the requirements not considered. For instance, taking a month or more to create a solution for analyzing and adjusting a marketing campaign that lasts only two weeks may provide little value. Analysts require tools that provide the flexibility to gather data and produce reports in formats they deem best suited for consumption with speed.


In addition to aligning with business strategy and delivering at the speed of business, it is important to factor in the types of individuals who will be working with data and their role within the organization. BI solutions and tools are seldom one-size-fits-all, frequently needing to support standardized reports, actionable dashboards, self-service reporting, ad-hoc analysis, and data discovery. For instance, sales professionals may require eye-pleasing visuals on a mobile device, which quickly show where the action is needed, and which include a guided experience to get to the root cause; an inventory analyst may require a self-directed experience with the ability to create content and derive new metrics for the organization; a restaurant manager may need IT certified printed reports. It is important to understand the different usage patterns of the BI solution and to provide tools to meet those needs.


While it is imperative to provide a conducive environment for flexibility and rapid development of analytic content, the importance of data architecture is not diminished. Given the explosion of data volumes – the rate at which data is created, and the importance of data security – it is more important than ever to create a robust data architecture to sustain an organization’s foundation for analytics. The data platform needs to provide the functionality to support structured and unstructured data and securely bring together on-premise data with external data or data stored in the cloud. While relying on traditional data orchestration methods of processing data in batch with ETL alone is not sufficient going forward, ETL tools will continue to be part of the equation. Evaluating the ability of ETL tools to orchestrate structured and unstructured data both on-premise and in the cloud is essential. Besides, the data platform will also likely need to accommodate some, if not all, of the following: support for required up-time and access to data, cleansing and de-duplication of data, in-place vs. scheduled batch processing of data, mobile access to data, and internal data consumption vs. externally shared data.


Organizations are increasingly looking at advanced analytics to gain a competitive advantage and differentiate themselves in the marketplace. With this, the analytics platform needs to support more than just traditional aspects of BI like reporting, querying, pivoting, slicing and dicing. It needs to enable going beyond being informative to being predictive and prescriptive, including such features as built-in Machine Learning algorithms and support for creating proprietary algorithms and visualizations through technologies such as R and Spark. Besides, the analytics platform should support the use of predictive and prescriptive analytics by the line of business users, not just data scientists, and provide the capability to incorporate advanced analytics models into the line of business applications.

BI and Analytics solutions can provide significant advantages to an organization for revenue growth, operational efficiencies, cost savings, and competitive advantage. Understanding the analytics needs and objectives of the business and providing the right tools and approach to meet these needs are key to a successful BI journey.