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Why Big Data, Advanced Analytics, and Business Intelligence Matter

Updated: Jan 22, 2019

Why Big Data, Advanced Analytics, and Business Intelligence Matter for You

“Our world is being flooded with data; don’t drown in the sea.”

What has your data done for you lately?

You don’t have to look far to find a projection illustrating the rapid deployment of sensors, connected devices, assistants, and other Internet of Things technologies. Nor is there a shortage of buzzwords surrounding big data, data analysis, and data utilization. The fact of the matter is that collecting data has never been as easy as it is today, and it is only going to become easier. Organizations both big and small are increasingly exploring, testing, and deploying new methods to gather and utilize data around them to gain and maintain the competitive edge in their markets.

Top-performing organizations are using data to find innovative new techniques to improve various areas of their business. This trend applies to organizations of all sizes and types, whether it be a large multi-national organization like Intel using predictive analytics to improve quality assurance for chip manufacturing or local SMEs like Twiddy and Company Realtors using operational data to offer rental property pricing strategies to customers. Government organizations are also exploring new ways to utilize data in areas like the fight against terror as well as preparing for disaster relief. Even the concepts around data collection, categorization, and sharing are being redefined as organizations collaborate to address macro-level problems like for example environmental sustainability.

The increasing relevance of effective data management and utilization can be seen in the rising levels of global spending on big data and data analytics (BDA) products, including commercial purchases of hardware, software, and services. A recent study from the International Data Corporation estimates that the 2017 global BDA spending levels of $151 billion are estimated to increase to over $210 billion by 2020, resulting in an annual increase of 11.9%.

Organizations looking to find new ways to utilize data in more effective ways to either grow, improve, or build should be aware of the distinctions between big data, advanced analytics, and business intelligence.

M&A Snapshot: Big Data and AI in the Energy Sector

Understanding big data

Big data can be understood as the vast and ever-growing sets of data collected by organizations throughout the world. Big data has generally been characterized by three distinct characteristics volume, variety, and velocity (“3 Vs”), as coined in the early 2000s by the industry analyst Doug Laney, seen in figure 1. Since the initial characterization of big data via the 3Vs, a variety of other organizations, researchers, and analysts have developed extended versions of the original characterization to include additional “V”s such as value, visualization, or veracity. One additional, “V” which stands out in this picture is volatility, especially when thinking of big data in an organizational context. The cyclical behaviors and natures of customers, markets, and governments all come with an inherent volatility which is a crucial element to understand and consider when using big data for decision-making purposes.

Despite the increasing amount of big data being collected, many organizations today only leverage a minute portion of these data sets to generate insights and affect change. Reasons for this shortfall can include insufficient analytical capabilities, technological constraints, and lacking executive mandates to pursue big data initiatives. Traditional data sets relied on by organizations include financial data, ERP/CRM generated-data, transaction records, and communication records. Big data however, enables organizations to integrate more complex and unstructured data sets into the picture, including social media feeds, sensor data, and video/image data

Understanding the Vs of big data

Figure 1. Understanding the Vs of big data

Applying advanced analytics

In line with the continued evolution of the generation and the availability of big data, advanced analytics are also evolving to meet the new requirements necessary to understand and utilize this data. What is advanced analytics and how does it differ from traditional forms of data analytics? As seen in figure 2, conventional forms of analytics focus on historical data and are seen mainly in descriptive and diagnostic types of data analytics. Advanced analytics differentiates itself by using the vast and mostly real-time sets of big data. Organizations able to effectively employ advanced data analytics techniques will improve predictive capabilities as well as be able to leverage prescriptive strategic recommendations based on detailed scenario assessments. This can be seen at companies like UniCredit, who employ prescriptive analytics to automate credit risk assessments or at companies like Ford, who model and forecast sales cycles and supply chain conditions. While traditional analytics remain as valuable foundations of an organization’s analytical workbench, maintaining a competitive edge will increasingly require organizations to employ and develop capabilities in advanced analytics.

Data analytics spectrum

Figure 2. Data analytics spectrum

Business intelligence

Now that we have covered big data and advanced data analytics, where does business intelligence (BI) fit in? Business intelligence describes the broader construct, as seen in figure 3, around using data to generate useful insights. This construct covers the complete gamut of technologies, processes, and analytical methodologies employed to create an organizationally relevant piece of information. At the front end, this includes sensors, operating systems, and processes used to generate and collect data both inside and outside of the organization. From there, collected data undergoes transformation and is integrated and stored in the data warehouse, ready for analysis. Finally, an organization’s applications, dashboards, and reporting tools can access and analyze both transformed and non-transformed data to generate business intelligence output which is then fed back into the greater data set. This output serves as the actionable intelligence used to enable data-driven decision making and is the significant item of value generated by a well-functioning business intelligence construct.

Figure 3. Business intelligence construct

Optimizing data strategies and capabilities

Organizations with the ability to develop, manage and maintain a business intelligence construct leverage historical, current, predictive, and even prescriptive views to make better data-driven business decisions. The real-world applications of effective data strategies and robust business intelligence constructs are plentiful. However, we categorize them into three broad categories: optimizing internal processes, building capabilities, and streamlining market activities. Figure 4 below shows just a few usage examples of how big data and data analytics are being used by organizations today. Use cases include automated decision-making, customer segmentation, product development, and even performance benchmarking against external peers.

Big data and data analytics examples

Figure 4. Real-world applications for big data and data analytics

Conversely, organizations with poor data strategies and ineffective business intelligence constructs could be paying a high price. Gartner, for example, estimates ineffective data governance resulting in poor data quality costs an average organization $13.5 million per year. While this example only covers a small portion of the overall business intelligence construct, the implication is evident in that the sustainability of an organization’s competitive edge will rely more heavily on its ability to collect and utilize the data assets at their disposal for value generation.

Capturing the immense opportunities afforded by the ability to convert big data into actionable insights or prescribed business strategies via useful business intelligence is quickly shifting from an innovative venture to a cornerstone of organizational success. With this in mind, it becomes clear that organization’s today must equip its people, processes, and systems with the capabilities required to collect and use big data actively. Best in class organizations will be seen utilizing these capabilities to create new value propositions by streamlining processes, optimizing customer interactions, and developing market strategies.

So, this brings us back to the original question, “What has your data done for you lately?”


About the Author

Chris is the Founding and Managing Partner of Peoples Partners and Associates (PP&A). Professionally, he has gained more than ten years of professional industry and strategy consulting experience working with leading companies. As a Management Consultant, Chris works closely together with clients to develop innovative strategic approaches to solve complex problems and find new ways to generate value.

As PP&A’s Managing Partner, Chris structures and leads consulting engagements by developing work plans focused on addressing client-specific issue sets and ensuring the subsequent execution of work plans according to specifications. Actively guiding multiple consultant and client teams in data collection, synthesis, and analysis efforts he derives and implements data-based solutions.

In addition, Chris leads an Energy Working Group for the Government Blockchain Association focusing on identifying unique problem sets and challenges faced by public and private entities regarding energy supply, distribution, and regulation. This group explores innovative blockchain and distributed ledger technologies geared towards addressing challenges while increasing system efficiency, security, resilience, and sustainability.

How we help

With the increasing importance of sound strategies and approaches for big data, advanced analytics, and business intelligence, PP&A offers a comprehensive set of data strategy, data analytics and data design services including: – Big data education, training, and executive workshops

– Big data advanced analytics

– Big data M&A venture and partnership development

– Big data and advance analytics capability assessment and development – Data strategy design and implementation – Big data use case ideation and valuation

– Business intelligence design and governance

To find out more don’t hesitate to reach out to Chris Peoples at:


© 2018 Peoples Partners & Associates. All rights reserved.


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