Why enterprises should redefine BI to unleash power of Big Data

(Editor’s note: contributed blogs like this are part of ChannelBuzz.ca’s annual sponsorship program. Find out more here. This article was authored by Martin Risau, senior vice president of the analytics & data management practice at HP Enterprise Services & Dragan Rakovich, Chief Technologist for the analytics & data management practice at HP Enterprise Services.)

Martin Risau, senior vice president of the analytics & data management practice at HP Enterprise Services

Martin Risau, senior vice president of the analytics & data management practice at HP Enterprise Services

While it has not always been the case, many organizations recognize the immense value big data and analytics can bring to their enterprise. Today, the ability to unlock value quickly without huge upfront investment becomes the real challenge.

The explosion of growth in the volume and variety of unstructured data and sensor data is outpacing the ability of many organizations to cope. As a result, they are facing intense pressure to improve their big data analytics expertise and capacity to remain competitive.

Growing capabilities in industrial scale analytics and data-driven insights are critical to survival in today’s age of the customer – and processing has to be achieved in real time. It’s a challenge acknowledged by most organizations. According to Forrester Research, business intelligence (BI) was the number one software priority for enterprise project plans in 2013.

Those that succeed in becoming data-driven and agile are well poised to transform customer and employee engagement and target new business opportunities. Failure to modernize and take advantage of these new data dynamics will put enterprises and their competitive advantage at risk.

A day late and a dollar short

Modern organizations are expected to continuously deliver better products and services, improve operations, effectively manage risks and create new business models to remain relevant.

To stay ahead in such an environment they must be able to analyze every piece of relevant data from new sources, such as human, machine and transactional; using that data effectively so employees and partners can create innovative new products and services.

Forrester Research[*] put it this way:

As the amount of data grows and companies’ ability to harness and create value from that data improves, firms will be able to optimize almost all aspects of business operations, including sourcing, logistics and the customer experience.

In addition, major dynamics like the digital revolution are upending whole industries; as a result, data-driven insights will become critical to business survival. Therefore, BI analytics is, and will continue to be, one of the main focus areas for companies’ investments and business strategies.

The traditional BI environment was designed to segregate analytics from transaction processing, yet enterprise warehouses became a core component to provide business intelligence, performance reporting and to a certain extent, analytics applications. But the focus tends to be limited to coverage of corporate performance and analytics based on transactional data after the event.

Legacy Enterprise Data Warehouse (EDW) systems will not disappear in the near future, if ever. Many business users are dependent on the rigorous performance reporting implemented from an ROI perspective and a replacement is not currently feasible. Additionally, some of the newer tools need to reach a level of maturity for production systems. On the other side, no enterprise can ignore the availability of a newer parallel processing platform that can run complex computational algorithms based on massive volumes of structured and unstructured data. Legacy EDW systems are simply not designed to provide insights from new formats and higher volumes of data on an industrialized scale, which has led to significant opportunities for newer technologies to overcome the challenges enterprises are faced with. For example, the inability to harness relevant insights and share it across the enterprise, potentially impacting business agility and competitiveness.

Successful BI has three parts

Overcoming these challenges may appear daunting and costly, particularly as there is a pressing need to become data driven and agile quickly. However, by taking an ‘as-a-service’ approach, changes can be implemented without significant upfront investments, regardless of where an organization is in the transformation journey.

The three key elements to a successful business intelligence modernization strategy include:

  1. Discovery environments that demonstrate how to bring data to a broader workforce or employee base and allow them to make data-driven and agile decisions. Environments include data lakes – storage repositories that hold raw data in its native format until required – data visualization tools, and services that enable rapid, enterprise-wide data sharing and analytics collaboration.
  2. Analytics solutions to help run the enterprise more efficiently, whether it be helping customers build something or making targeted improvements.
  3. Hybrid data management services to empower organizations to pursue business innovation through industrial scale analytics. This is integrated into business processes and systems to leverage all relevant information, whether it is from within the organizations’ transaction systems, social, sensor or streaming data.

Why early milestones are possible

The pursuit of an ‘as-a-service’ approach allows enterprises to protect themselves against infrastructure obsolescence.

Taking an open and flexible approach can combine the best of an organization’s existing business intelligence investment with the latest innovations in analytics to deliver real business value. This flexible consumption model enables organizations to quickly capitalize on business opportunities made possible by both traditional and new forms of data.

The modernization process can begin immediately and important milestones reached early in the journey.

For example, setting up a discovery environment in the data lake can take as little as two weeks, especially if using the cloud, followed quickly by new analytic solutions in an incremental manner.

This three-step approach minimizes the risk of a big-bang transformation and the associated costs. After 12 to 18 months, businesses can achieve a rock-solid, data-driven BI environment, operating with greatly improved total cost of ownership and service level agreements.

Big benefits with BI modernization

Software, hardware and consulting services combined with a BI modernization program can deliver lower, predictable costs and an increased ability to build enterprise-wide capabilities and differentiation. The transformed environment will support:

  • Greater data sharing across the enterprise to empower the workforce and foster innovation.
  • Embedded analytics to improve operational and decision-making processes, drive new insights and to provide integrated guidance real-time.
  • Risk mitigation capabilities through quick start options involving discovery environments and as-a-service deployment models.
  • Greater business agility to improve competitive advantage and customer interactions.

Big data on an industrial scale

The data deluge isn’t going to slow down anytime soon. Therefore, it’s crucial that enterprises improve their data processing abilities and combine the benefits of the traditional EDW environments with the opportunities newer technologies are presenting.

Implementing a BI modernization strategy with a strong emphasis on ‘as-a-service’, will ensure organizations can protect themselves from infrastructure obsolescence and avoid major upfront investment. Unleashing the power of data through industrial scale analytics and data-driven insights will allow them to optimize nearly all areas of operations.

The next wave of business innovation is waiting.

[*] TechRadar™ report BI Analytics, Q3 2013, Forrester Research, Holger Kisker, Vice President and Research Director, and Charles Green, Analyst

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