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AI, digital skills and data growth dominate the analytics agenda in 2020

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We are entering a new decade, one that will be defined by data. Organisations will either succeed or fail due to the way they collect, use and democratise data analytics throughout their business. At this pivotal inflection point of business transformation, organisations must embrace change and invest in it.

In the recently published 10 Enterprise Analytics Trends to Watch in 2020, MicroStrategy consulted leading industry experts to help us identify the key trends that will impact enterprise data analytics in 2020 and beyond, and three key themes emerged: the crucial role of Artificial Intelligence (AI), the focus on digital skills and the growth of data.

The role of AI and Machine Learning

AI is real and it is ready. AI and Machine Learning are going to play a key role in business data analytics and digital transformation, so the sooner organisations get going, the stronger their competitive advantage will be. Those organisations will also be building data sets from which to draw their insights and create accurate models.

During 2020, enterprises will be going deeper into data than ever before, thanks to AI. The key challenge in 2020 will be interpreting meaning, especially understanding human behaviour. Organisations that aim to differentiate themselves from their competition will need to understand how AI and Machine Learning can capture hidden knowledge on their customers, competitors, suppliers and market behaviour that impact performance.

In 2020, we will learn more about how the more mature enterprise will develop an autonomous ‘discover-to-improvement’ approach to AI and machine learning. Forrester finds that organisations with Chief Data Officers (CDOs) are 1.5 times more likely to use AI, Machine Learning and/or deep learning for their insights than those without a CDO. These organisations already have a significant head start, as they are building data models now and can gain an advantage from the insights they discover.

During 2020, we can also expect automated Machine Learning (AutoML) to come to the forefront in enterprise data analytics. By automating the entire process of developing and managing Machine Learning models, enterprises can produce simpler and faster solutions that can perform better than manually developed models. AutoML can increase productivity, reduce errors and help democratise the use of Machine Learning to non-data-savvy staff.

The demand for data talent is intense

The 2020 Global State of Enterprise Analytics report finds that three-quarters (75 per cent) of large enterprises and 59 per cent of smaller organisations (those with less than 1,000 staff) around the world plan to spend more on data talent going into 2020.

This is significant, because at the moment a third of UK businesses (32 per cent) say a lack of talent is a key barrier to more effective use of data analytics.

Data skills will be key to interpreting the data effectively, so organisations will increase investment in spreading data talent, confidence and ownership throughout the enterprise during 2020.

The Business Higher Education Forum (BHEF) and PwC expect to see around three million new job postings in data science and analytics this year. Organisations need to put in a place a strategy now to make sure they have the talent they need today and tomorrow.

Data will continue to grow exponentially

IDC predicts that the digital data that we create and consume will grow from around 40 zettabytes of data in 2019 to 175 zettabytes in 2025 – that is more than four times the amount of data produced in 2019.

To manage the incredible rise in data within the enterprise and use it effectively to help shape business decisions, organisations will need to combine data sources and break down siloes internally to share data. The pressure of increasing amounts of data will also drive the need for ‘embedded analytics’ in 2020, to provide every employee with the insights they need wherever and whenever they need them.

As data continues to grow exponentially, it highlights the need for a semantic graph. The semantic graph stores passive metadata describing the data in business terms, along with information on how that data is accessed and used, and by whom. It captures, organises and enriches metadata in a graph to derive business insights and recommend the most relevant content.

In 2020, the semantic graph will become the backbone that supports data and analytics in a fast-evolving environment – and a competitive advantage for accelerating the use and value of trending technologies such as AI, ML and the Internet of Things.

We are entering an exciting decade for enterprise data analytics, where data is now considered among an organisation’s most valuable assets. Those organisations that invest in data analytics talent and tools, master AI and Machine Learning, and empower their staff to access and discover insights will be the ones who will succeed in the 2020s. Those that do not face extinction.

Which will you be?

Peter Walker, General Manager UK & Ireland, MicroStrategy (opens in new tab)

Peter Walker is Chief Executive Officer, thyssenkrupp elevator.