Big data has the potential to transform business by enabling leaders to make data-driven decisions faster. According to the research firm IDC, organisations will spend $187 billion on big data and analytics technology by 2019, an increase of 50 per cent from the $122 billion invested in 2015.
Organisations are discovering new ways to tap into this potential to glean actionable insight and turn big data into a real-world competitive edge that will help them push ahead of their competitors. In a world where data is king, strategies that democratise data and put analytics in the hands of employees across the business yield the best results.
The implementation of ‘self-service’ BI has become more popular because it enables users across different levels and departments to act on insights and better serve their customers and partners. Once organisations decide to embark on this approach, many struggle to implement a complete analytics solution that meets the needs of their business users, executives, and IT. Instead they rely on a patchwork of point solutions, forcing them to inevitably grapple with data silos, complicated workflows, limited scalability, and lagging user adoption. These challenges, compounded with issues around data quality, poor performance, and unfriendly application design, prevent organisations from unlocking the full potential of enterprise analytics.
In order to avoid creating new problems that hinder, rather than enable, growth and efficiency, businesses must balance end-user flexibility with the performance and governance of a true enterprise-grade analytics platform. To be successful, organisations must establish roles and processes early, publish a verified system of record, and give business users the power to publish new data and dashboards in a governed environment. Organisations should drive adoption by first considering the needs of the user and then providing access to data via web, mobile, and desktop applications—all using a single, unified enterprise platform. By implementing this systematic approach, organisations can deploy intelligence everywhere, meaning data-driven decision-making replaces intuition at every level.
However, the journey to digitalisation is not without its potential pitfalls. As organizations seek to become more data-driven, they will start dealing with larger and larger volumes of data—making the availability of high-quality data more important than ever before. Unfortunately, this same increase in data volume makes it more difficult to quickly deliver data to analysts. That’s where automation through AI and machine learning can help. This software can support decision-makers by sifting through huge volumes of data and surfacing the most important insights, thus freeing up more time for analysts to create data-driven strategies and think critically about the business.
For organisations that are democratising data, ensuring data hygiene is also a massive challenge. Contaminated data can be a costly problem that takes significant time and resources to resolve. Minor inconsistencies introduced into data sets can have exponential effects across multiple departments, as users share unverified information with colleagues, clients, and others outside the organisation. Without even realizing that an issue exists, well-intentioned employees can make decisions based on faulty data that lead to a waste of company resources, increased maintenance costs, and distorted results.
‘Reverse engineering’ irrelevant, out-of-date, or erroneous data is a tedious, time-consuming process. It provides an opportunity for the competition to jump ahead because company resources are diverted to cleaning up and restoring data to its uncontaminated state. Of course, prevention is best. A robust and comprehensive data governance framework can ensure that every user across the organisation is granted the right level of access to the correct information. This, coupled with employee education, is the best way to ensure the quality of enterprise data and security is maintained.
The use of BI and analytics will only become more pervasive as companies recognise the potential benefits of implementing a holistic approach to harnessing and operationalizing big data. Choosing the correct technology is the key to ensuring that users get the most from content they create while operating within a governed enterprise BI ecosystem.
Francois Cadillon, General Manager, UK, Ireland, and Southern Europe, MicroStrategy, Inc.
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