The most impressive number is this one - 250% more business value generated by enterprises with a high level of data maturity. It's important to see mentioned data maturity and not any technological maturity, so these are not necessarily organizations that are fully digitally transformed, that have fully migrated to the cloud, that have data mesh or data fabric architecture or just created their data lakehouse. These are organization with a high level of Data Maturity. These are data-driven businesses, working with business-driven data. These are organizations that can get breakthrough improvements not just through their data, but mostly through the way they manage and explore their data.
In the modern world of business, data holds the key to success – but only if you know how to use it to your advantage. A study from Forbes Insights discovered that poor-quality data or the mishandling of data can leave businesses at risk of monumental failure. Moreover, poor data quality management currently costs businesses a combined total of $9.7 million per year. To harness the insights readily available at their fingertips and convert them into initiatives that set them apart from their competitors, companies must leverage superior analytical tools, resources, and platforms. Enter embedded analytics and embedded business intelligence.
Artificial intelligence (AI) is probably the most important new technology today. It has clear use cases, and the value that it's produced so far is indisputable – just think of the digital assistant on your phone, driverless cars, even Gmail uses it. With AI becoming more established, many organizations are starting to get access to and try their hand at running artificial intelligence initiatives. The business world is after all similar to an arms race, and having the latest'weapon' to help you get ahead of competitors is an irresistible prospect. The reason is that while successful, well-known AI projects may be capturing headlines (along with CIOs' dreams of digital transformation), the technology remains challenging.
Digital transformation is the profound transformation of business and organizational activities, processes, competencies and models to fully leverage the changes and opportunities of a mix of digital technologies and their accelerating impact across society in a strategic and prioritized way, with present and future shifts in mind. While digital transformation is predominantly used in a business context, it also impacts other organizations such as governments, public sector agencies and organizations which are involved in tackling societal challenges such as pollution and aging populations by leveraging one or more of these existing and emerging technologies. In some countries, such as Japan, digital transformation even aims to impact all aspects of life with the country's Society 5.0 initiative, which goes far beyond the limited Industry 4.0 vision in other countries. In the scope of this digital transformation overview, we mainly look at the business dimension.
When it comes to implementing and managing a successful BI strategy we have always proclaimed: start small, use the right BI tools, and involve your team. We know that the best approach is an iterative and flexible approach, no matter the size of your company, industry or simply a department. When encouraging these BI best practices what we are really doing is advocating for agile business intelligence and analytics. That said, in this article, we will go through both agile analytics and BI starting from basic definitions, and continuing with methodologies, tips, and tricks to help you implement these processes and give you a clear overview of how to use them. In our opinion, both terms, agile BI and agile analytics, are interchangeable and mean the same.