Big Data: changing the future of business models

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Worldwide data is predicted to grow ten-fold over the next seven years, bringing a raft of opportunities and challenges for businesses across a multitude of sectors. If embraced properly, it can provide infinite benefits.  Developments in big data and data analytics have already shifted the way that businesses operate, and this will continue to grow; the potential for transformation is vast. 

Traditionally, firms have used analytics in the form of summary or descriptive analytics and Business Intelligence (BI)/Management Information (MI) on structured data. However, the growing number of challenges businesses face today mean that traditional analytics would not even scratch the surface. As a result, firms will need to review their current status and adapt to these developments or they will lag behind in competition and innovation. 

Unbundling Data Analytics 

Data analytics is the process of analysing and drawing conclusions from data with the aid of technologies and software. Historical records or new information can be processed for real-time use and insight.

Data science as a principle has existed for years, but many techniques such as machine learning, neural networks and deep learning, have only recently been propelled to the forefront of most business agendas. 

Data Gravity, the term coined by software engineer Dave McCrory in 2010, paved the way for a great analogy of the nature of data and its ability to attract additional applications and services. The Law of Gravity states that the attraction between objects is directly proportional to their mass. When applied to data, it describes the way the number of services, applications, and even customers, attracted to data increases as the mass of the data increases. 

Put simply, applications and data are attracted to each other and the more data there is, the greater the attractive force pulling applications and services to associate with that data.

Since its inception, the business community has reaped the benefits in numerous ways.  

Discovering the Opportunities 

The ability to analyse and make informed decisions from the use of data and its analytical capabilities is vital if a business is to succeed. In an increasingly competitive industry, it is imperative that firms are able to make quick and increasingly complex decisions to cater for the changing demands from customers and evolving market conditions. 

By harnessing data, businesses can identify new opportunities within their existing business operations, create more efficient operations, increase profitability and improve customer service. By embracing data, businesses can gain a competitive edge over their rivals, ensuring they don’t lag behind the competition.

Over the years, our data team has worked alongside businesses to help them find data-driven solutions and technologies with the aim of fast tracking their objectives and stimulating growth. As a result, we’ve outlined the most notable benefits of embracing data analytics below:  

  1. Velocity: the speed of data allows for real-time results, enabling businesses to make quicker and well-informed business decisions. 
  2. Streamline Business Operations: data helps understand, streamline and improve business processes, reducing cost and time.
  3. Track Performance: collecting and reviewing data allows businesses to continually track business performance.
  4. Problem Solving: tracking performance allows businesses to breakdown performance and as a result, businesses can identify which areas aren’t performing well and take the necessary steps to rectify.
  5. Market: data helps businesses understand consumers and an increasingly competitive and changing market.
  6. Variety: data comes in a variety of formats, which may be structured to best fit an organisation. Businesses can tailor the data in line with their business objectives.
  7. Value: the usefulness of data will continue to be explored, bringing infinite possibilities to businesses that embrace it.

Data analytics help businesses to increase revenue, efficiency and optimise workforce ability which consequently helps business to meet their objectives. As data analytics become more advanced, the benefits will increase, paving the way for previously unattainable results and capabilities.  

Identifying Challenges 

Big data is no longer just a trend and while far from being fully established, it is something that an organisation needs to factor into its architecture design and embed into its business model. 

The key to successful implementation lies in businesses embracing the challenges inherent in the six Vs - volume, velocity, variety, veracity, variability and value. This will ensure businesses are not just addressing the challenges but are tackling them head on to thrive in the digital space.

  1. Volume - The technical limitations of storage capabilities in existing management systems mean they cannot cope with such volumes.
  2. Velocity - Processing capacity and processing technology of data currently hinder demand.
  3. Variety - Existing database management systems are used to dealing predominantly with structured data. With the breadth of data currently present, database management systems face challenges in dealing with such data. 
  4. Veracity - The uncertainty around the quality of data poses a big challenge to businesses that need trusted and reliable data sources.
  5. Variability – The inconsistency indicator that makes the case for Big Data vs traditional BI/MI approaches.
  6. Value – What is the data worth and how is it useful in helping businesses achieve their wider objectives. 

The final V, value, was not part of the original framework and is often ignored, but over time it has been recognised as equally important in terms of understanding how big data can help a business, and often change the business model. Value can be realised by extending the existing analytics system, enabling the organisation to integrate and utilise innovative data management and analytics technologies. 

Changing the Narrative 

Data science as a principle has existed for years, but many techniques such as machine learning, neural networks and deep learning, have only recently been propelled to the forefront. Analytics has also been extensively used for decades, but the ceiling has always been the ability to predict and go further than summary and descriptive analytics.

The need for innovation and differentiation has led firms to become insight-driven rather than data-driven. A key enabler for this has been the application of advanced analytics and use of machine learning techniques that can understand huge amounts of data. As a result, a combination of methodologies, tools and domains, all coined under the label of data science, have been developed. The next challenge is now using big data and all of its benefits to mould, shift and shape respective business models to form a structure which will facilitate growth opportunities.

Evangelos Tzimopoulo, Senior Manager at Brickendon

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