CHOOSING AN APPROACH TO ANALYTICS: is a single technology platform the right investment?

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    CHOOSING AN APPROACH TO ANALYTICS: is a single technology platform the right investment?

    There is virtually no debate about the business value of analytics. Effective analytics provide insights into what happened, why it happened and what is likely to happen in the future, as well as the factors that could help shape different outcomes. But when it comes to the “how” of analytics—including which technology platform(s) will be used to support them—there is far less clarity. In this article, Abhishek Bhattacharya explores some of the fundamental challenges of building an analytics capability, including the pros and cons of investing in an all-encompassing technology platform.


    Although technology platforms for analytics is the focus of this article, it would be a disservice to readers not to acknowledge that technology represents only a piece of the picture. When it comes to building an analytics capability, the real complexity lies not in the technology but in the business case and the supporting analytics models. To be successful, every analytics initiative must start with a clear understanding of appropriate business cases. How and where will the analytics be used? What are the critical performance indicators and/or business questions that must be measured and analyzed? From there, sources of data must be identified, and data must be modeled—that is, structured appropriately for the analytics engine. Analytical models, also known as quantitative models, are a key difference between traditional descriptive reporting and more sophisticated analytics, including those that can help predict or optimize outcomes. With those models in place, the next challenge is ensuring the quality of the data that enters the analytics engine. The phrase “garbage in, garbage out” applies here. If data is sub-par quality, the output will be too, and business stakeholders will lose trust in the analytics.

    All of those steps must be executed for every new business problem, and all are technology agnostic. Together, they represent about 80 percent of the effort within any analytics initiative. The remaining 20 percent focuses on technology: choosing a platform; creating a production and testing environment; and conducting performance testing and tuning. The core steps of the effort should help inform the technology decisions—and one of the most fundamental is whether to build a one-size-fits-all platform or to develop a series of platforms, each designed for a specific requirement or set of requirements.


    Opting for a single enterprise analytics platform can seem like a logical decision—a way to ensure consistency and cost-effectiveness across all of an organization’s analytics initiatives. Yet an allencompassing platform is unlikely to succeed for a number of reasons.

    The first reason is the sheer diversity of analytics needs. An organization may be able to address its current range of needs, but it can be difficult, if not impossible, to anticipate all of the possible types of analytics it will need in the future. Building a platform around all of those theoretical “needs” would also come at a very high price in financial terms.

    Second, a one-size-fits-all platform could cost the organization in terms of opportunity. As the user community becomes more proficient in analytics, they will ask for advanced features and capabilities. In most cases, that involves an evolution from descriptive (“what happened?”) to predictive (“what is likely to happen?”) and prescriptive (“how can we increase the likelihood of our desired outcomes?”) analytics. Building incrementally as these needs arise is a much more palatable solution. An incremental approach also leaves open the opportunity to tap into ongoing innovations. Technology platforms represent a fast-moving, everchanging landscape, where committing to a single stack can cost you the chance to leverage something newer and better.

    Finally, the advent of cloud computing has revolutionized the way an analytics environment is set up and a technology platform is built. The cloud has significantly reduced the work so that it is now possible to have an environment up and running in days, if not hours. It affords real flexibility, with the ability to grow a platform to support additional users and new types of analytics. It also makes it easy to start small, building credibility and momentum over time.

    For all of those reasons, building an enterprise platform for analytics is probably not well advised. However, the opposite approach—building each component individually and then harmonizing those components— can be equally expensive and ineffective. Many of those high costs are spent in integration, data movement and harmonizing the components.


    If neither an all-encompassing platform nor a conglomeration of platforms is the right approach, how should organizations proceed? The key is to strike a balance between building everything and building the bare minimum. Ideally, such an approach would yield an all-encompassing architecture (see Figure 1) that does the following:

    • Embraces layers. Rather than focusing on the platform specifically, think in terms of the layers of any analytics capabilities. An effective architecture will include layers for data, data ingestion and business intelligence. Compared to traditional techniques, this approach affords much more flexibility over time. In particular, when traditional star schema are used, everything is driven out of the schema—making it difficult to evolve the platform as analytics needs change.
    • Offers components within each layer. For greater effectiveness and agility, each layer should be built with modular components. The data layer must provide the ability to manage structured, analytical, unstructured and streaming data. The data ingestion layer should have modules for master data management, extract transfer and load (ETL), and data quality management. The BI layer must offer self-service, various types of analytics, visualization capabilities and support for multiple device types.
    • Is designed for evolution. It is important to build an architecture that can easily accommodate change. As part of that, work to build an understanding of the dimensions of potential change (business problems and types of quantitative models, for instance). By understanding the aspects likely to change, you can identify appropriate components and technologies at each layer. Fortunately, modern technologies—from columnar databases to the Hadoop open-source software framework—are inherently flexible and do not force every part of the solution to be tied to a specific quantitative model or schema.
    Figure 1: All-encompassing analytics architecture.


    Every successful analytics initiative will be built upon a sound framework that includes identifying business value, building models, sourcing data and, ultimately, driving adoption. At every step, technology is a critical enabler, but it should not be the central focus. Nor should it be a barrier. With many analytics technologies now available on the cloud, it is possible to get started with very little upfront capital cost. Using Amazon Web Services, Azure and other cloud services, an organization can begin to build an allencompassing architecture—starting small, building something and showing value to the business before making substantial investments.

    The Author
    Abhishek Bhattacharya

    Abhishek Bhattacharya
    is a Vice President of Technology based in Noida, India and leads the Technology Practice at Sapient Global Markets. Abhishek has spent the last 15 years architecting and designing technology solutions for companies around the world. His team is focused on developing market-leading solutions and frameworks for financial and energy services companies.

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