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Addressing the Modern Data Complexity Challenges in Business Analytics

It is 2018, and it is pretty clear that data and analytics are playing a much more significant role in business than they ever have; today data has become a driving force in decision making for businesses of all kinds. Organizations can no longer drive business decisions based on their gut and intuition; they need to capture relevant information and use it to make smart, strategic, quantifiable decisions. However, the volume and complexity of business data is growing, making it difficult for companies to make sense of it for strategic decision-making. IDC estimates that by 2020, the amount of information stored in the world’s IT systems will be enough to fill a stack of tablets that reaches from the earth to the moon 6.6 times. In order to improve performance across departments, organizations are left with no choice but to apply several data-driven methodologies for faster and better access to data.

What is data complexity

Data that is being generated across the world is growing exponentially. IDC reports that compound annual growth of data will be almost 50% per year. Data complexity primarily depends on two major factors: how big the datasets are and the number of disparate sources that they come from. Reports suggest that on an average, organizations have to integrate data from more than 6 disparate sources. In the healthcare sector for instance, humongous amount of electronic data gets generated every single day: from insurance claims, to patient prescriptions, hospital bills to lab results, clinical trials to research data, patient diagnosis to treatment data. What’s more, the data is collected in too many different formats and are part of various silos: physicians’ notes, individual EMRs, disparate CRM systems, paper records and more. This causes gaps in the information, slows down the decision-making process, and reduces the effectiveness of business performance.


Why is it increasing

The data that organizations are dealing with today is far more complex and diverse than it was a decade ago. Today’s data is no longer confined to manual records and Excel spreadsheets but is spread across several complex systems; this complexity of data indicates the difficulty organizations face while trying to convert it into business value. Data complexity is increasing due to a number of reasons:

  • As more industries today move to digital and online business models, there is a plaguing need for them to become more measurable and data-driven.
  • Technological advances are producing new data sources making it challenging for organizations to gather, store and process this ever-evolving data.
  • There is a sudden increase in the need to analyze several types and forms of data from various sources and use it to build strategies across an increasing number of business scenarios.
  • Humongous amount of data is being generated from several sources like machine data, social network data, customer data, as well as data generated by AI, mobile, and the Internet of Things (IoT) that is adding to the complexity.
  • As the data-driven culture grows, business leaders are constantly under pressure to justify and back their decisions with strong data points.

How businesses are dealing with it

The fact that data has grown both larger in size and more diverse in nature creates new challenges in the world of data analytics; making sense of this highly complex data requires a deeper understanding of the way data is extracted and structured before any analysis can be done.

  • Innovations in business analytics are allowing businesses to gather, store and access vast amounts of data – collected internally as well as data from external data sources.
  • Big data analytics is aiding organizations in analysis and visualization of unstructured data and is providing organizations with the much-needed data-driven insights.
  • Machine learning and predictive analytics are helping analyze untapped data sources and are offering new insights for strategic business decision-making.
  • Self-service data analytics tools are helping organizations in simplifying the process of combining multiple sources of data, presenting the results in attractive visual formats and enabling them to reach new and unexpected conclusions.
  • Data mining tools are helping unearth large amounts of data, enabling businesses to find patterns, forecast results, and improve the decision-making process.
  • Modern data storage units are allowing businesses to efficiently store, consolidate and process data from a variety of sources and auto-update records at regular intervals.

What you can do

In a business world that is constantly plagued by data complexity, leveraging the right tools and technologies can help a great deal in achieving the insights you need. Start by getting familiar withdata complexity and understand the various methods you can use to analyze this data. Implement the right tools and be sure to use the right for modeling and advanced analysis. Once you are familiar with data, you will be in a better position to streamline information, drive data-driven business decisions easily and more effectively and improve your bottom line in the most positive way possible.





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