Why Big Data demands new technology

Big data analytics looks at large amounts of data to uncover hidden patterns and other insights to help improve business performance. The concept of big data has been around for years and most organisations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. There’s no question that data is changing the way businesses work.   

Thanks to the technology available to big brands, it is now possible to analyse data and get immediate answers. Data can be used to make smarter business moves, drive more efficient operations and even keep customers happy. In turn, this all helps increase profits. As the role of data evolves, more businesses need technology to organise and analyse their data.    

Perhaps the most significant advantage of data analysis is the opportunity for cost reduction because data can help identify more efficient ways of doing business. 

Faster, better decision making is another advantage. The speed of some technologies, combined with the ability to analyse data in real time, allows businesses to make immediate decisions based on what they’ve learned. 

The ability to measure customer needs and satisfaction through analytics empowers businesses to give customers what they want. This can mean new products and customer-driven services based on reliable data, which can help businesses grow. 

One future big data trend that demands technology is predictive analytics. It’s vital that businesses have access to predictive analytics.   

Software or hardware solutions that can be used to discover, optimise, and deploy predictive models by analysing big data can be hugely beneficial to businesses. It can help to improve business performance, mitigate risk and enable evolution. 

Predictive analytics is a machine-learning technique that can identify the likelihood of future outcomes, based on historical data. Therefore, its greatest advantage is providing the most accurate assessment of what will happen in the future, so organisations can feel more confident in their decisions. This new technology can also help businesses discover previously unnoticed patterns of behaviour. Some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing. 

Most businesses have data management technology but to begin with, the data needs to be high quality and well-governed before it can be reliably analysed. With data constantly flowing in and out of a business, it's important to establish repeatable processes to build and maintain standards for data quality. 

There are a few key tools that can make your data work as hard as possible and provide the best possible analysis. New tools and improved approaches across data analytics are always popping up and offering ways to deal with the challenge of big data and some areas are experiencing more innovation and investment. 

There are two current big trends in data that demand new technology. First, is the emergence of highly targeted solutions for specific industries, from analytics-based software and service providers that are helping their clients achieve a more direct, and at times faster, impact on the bottom line.   

Specialist models are being built that have a clear business focus, and can be implemented quickly. We are seeing them successfully applied in a wide range of areas from logistics and risk management to pricing and personnel management. Although a shift in industry culture is still needed for fluid technological integration, the more focused tools represent a big step forward.

Visualisation tools are putting business users in control of the analytics, by making it easy to slice and dice data. Such tools can help define and explore the data, which is often needed to address the business issues, and support decision making. Data sources are being designed to deliver information from a large number of information revenues, distributing data stores in real time for real results.   

Finally, it’s becoming much easier to automate processes and decision making. Big data that was previously unreadable is now easier and quicker to decipher. Technology improvements are allowing a much broader capture of real-time data (for example, through sensors) while facilitating real-time, large-scale data processing and analysis. These advances are opening new pathways to automation and machine learning that were previously exclusive to high profile technology companies but are now accessible to smaller businesses too.  

The other trend is the technology used, as gathered data needs to be understood. Powerful data collection platforms certainly help to sort, relate and quantify data but no matter how well the data is presented by a system it still requires analytical skills to make it meaningful. The data specialist can draw out key indicators and distinguish them from false positives and background noise.  

Once data has been interpreted, the big question becomes how to act on that information. The data specialist has to combine their understanding of the data with their knowledge of the business to help develop concrete business strategies. This may come in the form of creating new sales approaches, discovering entirely new markets, or implementing automated features that respond intelligently as data is gathered. There are two areas of data gathering that should be highlighted in these new technological advances.  

  • Data preparation: software that helps with sourcing, shaping, cleansing, and sharing diverse and complicated data sets to accelerate their use for analytics.  
  • Data quality: products that conduct data cleansing on a large scale, using huge data stores and databases.  

There are many different platforms that specialise in these vital areas, and which platform is ideal depends on the individual requirements of the business.     

There are a variety of different data analysis tools and software available on the market right now which can really make a difference in your workplace. In order for that to happen, however, a data literate workforce is vital, so it might be worth investing in tutorials to help your colleagues better process the data in front of them.   

Business leaders need to understand that the best-informed decisions are made when data is involved, and is accessible to the majority. Big data can be broken down, deconstructed, and analysed in the best way for your company. Targets can be met with data analysis and organisation tools – so long as they are understood. So, take the time to get to grips with big data analysis, and see how it could improve your business. 

Philip Woods, Director, KRCS Group Ltd  

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