Business Transformation: How Big Data Analytics Helps

Many enterprises worldwide are using big data analytics for implementing operational strategies and facilitating business transformation. IDC, with support from SAP and Intel, conducted a survey to study how organizations are using big data and analytics solutions to promote business transformation and best practices.

IDC has a big data maturity model, which can be used to assess organizations based on five parameters: intent, data, people, process, and technology. The assessment helps in determining the effectiveness of their big data and analytics initiatives and understanding the extent of their progress. Also, organizations can be compared with respect to the said initiatives; this comparison allows for benchmarking and sheds light on how a certain enterprise can improve its analytics maturity and relevant success rates.

Analytics and Business Strategy

In one of my previous articles — Big Data Analytics for Big Retail Success — I have discussed how data has evolved over the years with respect to type, volume, and velocity. This rapid evolution can be attributed to the widespread digitization of business processes, globally.

The real-time analysis of data using analytics solutions is enabling enterprises worldwide make quick and effective business decisions. Gone are the days when analytics was merely used for historical data study. We are presently living in the era wherein enterprises possess strong forward-looking capabilities, thanks to the power of predictive analytics and advanced data science.

The white paper — Building an Analytics First Organization — discusses the significance of embedding analytics into the organizational DNA; in the digital age, being data-driven is imperative for sustaining business growth. The effective deployment of big data analytics strategies is the key to driving business transformation, as analytics enables organizations to keep up with changing business needs and the rapidly evolving technological landscape.

Big Data Maturity

The success of big data strategies depends on the maturity of an organization in terms of analytics deployment, people, technologies, best practices, and so on. This maturity level is proportional to an organization’s ability to achieve business transformation with the help of big data.

IDC has defined five stages to identify the maturity of an organization with regard to its big data undertakings as well as the steps it can take in order to mature further.

1. Ad Hoc: Big data initiatives are driven as and when a business requirement arises. There is an absence of a specific analytics deployment model, individual roles are not properly defined, and there is a lack of a measured approach to resource allocation.
2. Opportunistic: There is a clearer understanding of the business goal. However, there still exist inefficiencies in project management and resource allocation.
3. Repeatable: Big data strategies are defined for various business undertakings. Also, there is sufficient budget allocation and stakeholder buy-in.
4. Managed: Analytics deployment models and big data strategies are more in line with enterprise goals. Best practices are established, and significant ROI is generated consistently through the said strategies. The organization becomes data-driven and analytics becomes a part of its operational DNA.
5. Optimized: Big data and analytics initiatives are completely operationalized. There is a highly efficient center of excellence in place to govern these initiatives. The organization uses disruptive deployment models and technologies to create a significant impact in its market. The focus is now on innovation and continual business transformation for achieving unprecedented business value and impact.

Big Data Innovators

The survey conducted by IDC saw the participation of 1,810 organizations. IDC reported that of these organizations, only 59 fell under the category of ‘advanced organizations’ with respect to big data maturity. Big data innovators fall under this category as well; in terms of the maturity framework discussed above, a major percentage of the innovators are in the managed stage, while others are getting closer to crossing the repeatable stage and a handful are almost at the optimized stage.

Big data innovators have identified three pillars that support business transformation — profitability, high operational efficiency, and customer centricity. Early adoption of emerging technologies will help in streamlining and improving analytics processes, which will, in turn, improve operational efficiency and decision-making. Moreover, access to real-time data helps organizations understand customer behavior and requirements quicker, thereby keeping them a step ahead of their competitors. Thus, it enables them to truly transform with respect to changing market dynamics, improve customer retention, and increase profitability.

Big data innovators exhibited many similarities with regard to their basic outlook and approach toward data and analytics as well as business outcomes. Some of the foremost similarities are listed below:

• The ROI from big data analytics initiatives is generated faster — within six months of deployment.
• Their data is high in quality and relevant to business objectives. Also, their data mining and warehousing capabilities are secure and timely in nature.
• Most of them have an enterprise budget in place for big data and analytics projects.
• The adoption rate of advanced analytics technologies, with sound visualization, predictive, and real-time capabilities, is considerably higher. The same applies for data management technologies as well.
• There is support from various organizational levels — right from executive to IT — for big data and analytics initiatives.
• Continual advances in technology are considered an imperative for nurturing business transformation and innovation.

Technology and Business Transformation

There is a correlation between the effectiveness of analytics projects and the adoption rate of advanced technologies. Technology acts as an enabler for business transformation; advanced analytics technologies make organizations more agile in terms of keeping pace with changing business trends and customer needs.

According to the IDC survey, 71% of the big data innovators use advanced analytics tools that have high-level forecasting and real-time visualization capabilities. The usage of such technologies, therefore, gives enterprises a competitive edge and the ability to foster a more customer-centric approach.

Growing digitization will lead to the creation of new data sources, which means there will always be more data to warehouse and analyze. This makes deriving value from big data projects all the more challenging. It is, therefore, no surprise that 93% of the organizations surveyed by IDC fell under the opportunistic and repeatable stages of big data maturity. In this scenario, a technology-led business transformation will be critical to sustaining higher levels of big data and analytics maturity. Also, data warehousing should be improved by ensuring the availability of different data management systems for specific data types.

Analytics as a Service

Establishing a unit specifically for big data operations is not necessarily a part of every enterprise’s agenda. Most companies either lack the required technical competencies or do not wish to make the investments required for in-house talent and resources. These companies have the luxury to become data-driven nonetheless, thanks to analytics as a service (AaaS).

The presence of analytics service providers has made advanced big data and analytics technologies more affordable, accessible, user-friendly, and, therefore, democratized. Moreover, AaaS helps organizations deploy big data and analytics projects with minimum disruption in ongoing business processes, thereby allowing for seamless business transformation.

Think Bumblebee from the movie Transformers in which he goes from being a mean sports machine — the Chevrolet Camaro — to a highly efficient and multifaceted alien robot, within seconds. This is what AaaS does for enterprises; it enables them to achieve business transformation quicker without any decline in operational efficiencies.

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This write-up first appeared on the BRIDGEi2i blog.