Analytics and Data Sciences

WInvesting decisively in Digital across value chain, with a particular strategic focus on harnessing data as an enterprise-wide asset can greatly improve the maturity of an organisation, including cost-efficiencies, risk-reduction, competitiveness, and collaboration with internal and external stakeholders.

Often organisations suffer from capturing too much of data and not having enough utility or information generation from that massive amount of data. It often creates negative value since the organisations then feel that enough is being done to capture and use data for decision making, resulting into wasted opportunity. Additionally, there is a huge investment required for capturing and managing the data.

With a clear focus on three to four highest value use-cases, organisations can deliver clear value through a manageable amount of data that effectively translate into the desired information for decision making.

Also, organisations often have too much of importance given to systems. VCG thinks of the system as a combination of processing-features and data-sets. The datasets stored across the various systems must be ‘Mutually Exclusive’ and ‘Collectively Exhaustive’ (MECE). This MECE principle guides the way we design and deliver system related capabilities.

Our Data architects and product designers work together to integrate product design process with the data architecture development process, providing a capability that effectively supports the business needs.

We specialise in:

  • Developing data-repositories and information architecture
  • Asset-information capability design and development
  • Building Information Modelling (BIM) solutions
  • Minimum Viable Product (MVP) development to interoperate multiple data sources
  • User-centered design with the use of such technologies as Artificial Intelligence and Machine Learning, and Natural Language Processing (NLP)
  • Co-design workshops and continuous development methods

Data Analytics

Data analytics is the science of analysing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimise processes to increase the overall efficiency of a business or system.

Data analytics is important because it helps businesses optimise their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.

A company can also use data analytics to make better business decisions and help analyse customer trends and satisfaction, which can lead to new and better products and services.

Artificial Intelligence

Artificial intelligence (AI) is a collection of technologies that excel at extracting insights and patterns from large sets of data, then making predictions based on that information.

That includes analytics data from places like Google Analytics, automation platforms, content management systems, CRMs, and more.

AI exists today that can help businesses get much more value out of the data they already have, unify that data, and make predictions about customer behaviours based on it.

Natural Language Processing

Natural language processing (NLP) is a branch of AI that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.

NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important, in any human language. It can analyse more language-based data than humans, without fatigue and in a consistent, unbiased way.

Internet of Things

Internet of Things (IoT) data analytics is the analysis of huge data volumes generated by connected devices. Organisations can derive a number of benefits from it: optimise operations, control processes automatically, engage more customers, and empower employees.

A huge variety of devices connect to the internet and share data through sensors every day. This data is worthless without analysis. However, with an IoT analytics solution put in place, the data that organisations produce is effectively collected, analysed, and stored.

As a result, it allows organisations to optimise their operations at all levels, improve decision making, and achieve a number of benefits. To improve equipment maintenance, the combination of IoT sensors and data analytics may help companies, especially in the manufacturing industry, to determine when equipment requires maintenance by measuring vibration, heat, and other important figures. Smart equipment can also send messages to operators about potential breakdowns, wear, and delivery schedules.

This not only facilitates regular equipment maintenance but also contributes to predictive maintenance. Sensor data is used to predict when assets need to be serviced, which allows maintenance to be scheduled at the optimal time, thus reducing breakdowns and saving maintenance costs. IoT allows workers to see exactly how their machines are performing in real-time, and alerts them to any issues that might be arising. Being able to prevent unscheduled downtime by using predictive maintenance can provide significant benefits.

Big Data

Big data analytics is the complex process of examining large and varied data sets, or big data, to uncover information – such as hidden patterns, unknown correlations, market trends and customer preferences – that can help organisations make informed business decisions.

Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by high-performance analytics systems.

Big data analytics applications enable analysis growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional business intelligence and analytics programs. This encompasses a mix of semi-structured and unstructured data — for example, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the IoT.

Speak to VCG Digital to learn how data analytics could apply in your business in a time when organisations are looking to gain a competitive edge, improve processes, drive innovation, and ultimately enhance customer experience.

how can we help you?

VCG Digital has been instrumental in guiding our Digital change and BIM initiative. This has allowed us to invest objectively with an eye on value and focus on outcomes for the end-users”

, a Water Utility organisation