Unleashing productivity and profitability with big data and analytics

February 23, 2021

Big data analytics examines large amounts of data to uncover hidden patterns, correlations, and other insights. With today’s technology, it is possible to analyse your data and get answers from it almost immediately – an effort that is slower and less efficient with more traditional business intelligence solutions. There is no single technology that encompasses big data analytics. Of course, there is advanced analytics that can be applied to big data, but in reality, several types of technology work together to help you get the most value from your information such as machine learning, data management, data mining, in-memory analytics, predictive analytics and text mining.

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Big Data as defined by Hadoop has failed to deliver business benefits. Hopefully, this will be the last time that business leaders and managers will hand over the IT budget to the IT department which tells them they have a silver bullet solution. They and the data scientists are responsible for squandering inordinate amounts of money on a solution that even common sense would tell you was never going to deliver repeatable results or business value. Fortunately, advances have been made which have produced software which is capable of storing large amounts of data, getting it to the right place, at the right time and allowing analytics, Machine Learning and Artificial Intelligence work to be undertaken by people who are not from the ranks of the extremely technical division of IT technologists. Also, current technology allows data lineage to be effectively and automatically mapped and controlled. This presentation is about a data platform that will allow businesses to use their data to do all these things without handing control to IT functions, that by definition, could never share their business objectives.
You’ve probably heard that data is the new gold. In this webinar we will explore the journey that a company must take in order to embrace digital, generating and collecting data in a scalable fashion and using this data to drive decisions.
2020 saw the world of business upended by the pandemic. With many organisations at the brink of collapse, firms have been forced to scale back their workforces, whilst making other cost-saving measures simply to stay afloat. Now, as organisations look to the future, the pressure is on the c-suite to not only perform and build resilience, but also to fuel future business growth – and for many, success will be entirely dependent on the decisions made over the coming weeks and months. But how can the c-suite achieve this when they are often relying on unstructured data to make these decisions? According to statistics, 89% of IT leaders report data silos are creating business challenges. When it comes to decision making, data that is captured in real-time and analysed effectively is a necessity. After all, any inaccuracies that mean you are unable to turn data into reliable information renders that data utterly worthless. Ultimately, the value in data lies not in the data itself, but the information and fact-based decisions that can emerge from it. Sanjiv Sachdev will explore how business can unlock the true value of data, the insights the right data can unlock, and why it is a competitive business advantage. The importance of transforming data into future insights, and how this can be achieved. The role of reflective vs. proactive data and how IT Finance Management (ITFM) solutions can be used to effectively analyse data and bridge the gap between business and technology.
Once upon a time, data within the manufacturing and industrial sector could be easily locked up in filing cabinets. Today, the swift expansion of industrial internet of things and cloud technology has resulted in an accumulation of sensitive data; much of which is prime for analysis to improve the efficiency of existing technology and instigate the innovation of new solutions. Unfortunately, with more data, complex regulatory pressures follow. More often than not, these regulations limit the ability to share sensitive data with external parties for collaboration. If sensitive data can’t be shared, how do we train machine learning models? In this webinar, Nicolai Baldin, offers practical tips that will enable organisations to build modern data architectures, whilst maintaining ethics and privacy at the core of these initiatives.

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