The corporate IQ depends on data, as does the very purpose of many companies that have already declared themselves on a journey to become “data-driven”. In order to thrive on data, data needs to be managed as a key corporate asset above anything else – with the same passion as with any other asset in the organization. Then, sources and outlets of data need to be mastered, as they are the essential powerlines to any organization energized by data. In any case, data means perfectly nothing, unless it is activated for business through algorithms, insights, decision support and intelligent automation. And as there are no clear separations anymore between consumers and providers of data in a Technology Business organization, many may need to up their game and become a bit of a data scientist and data engineer. Finally, the raw potential of data needs to be carefully handled, as it may have many good and bad sources and equally, many good and bad ways to activate it.
Technology innovations from the open-source community have created what the entire world has come to know as ‚Big Data‘, already an almost archaic name for a common set of technological capabilities to ingest, store, access and analyze data from many different sources, in all sorts of different formats with all sorts of different timings.
With that technology playing field now firmly established, it has triggered enterprises to realize data may be at the very heart of their Technology Business ambitions, not seldomly leading to becoming a self-declared ‚data-driven‘ company. So, it becomes crucial to understand where data comes from; not only from internal but also external sources, and maybe even as synthetic, generated datasets.
In Technology Business organizations, the best insights are created in the closest proximity to the business and to do that, data must be discovered, prepared, analyzed, and visualized right there – and nowhere else. Unfortunately skills are rare, and secure, high-quality access to the right data is far from a given. Both AI and automation, together with powerful, high-productivity self-service tools come to the rescue, making potentially everybody within the organization more data-savvy.
In order to actually activate data and bring it to life within a Technology Business organization – it’s all about data science and next-generation algorithms too. An eclectic catalog of high-performance analytics is destined to be a key component, whether it’s built inhouse or mindfully acquired from elsewhere. New, sometimes highly unconventional, AI ways of creating insights from data (such as deep learning and reinforcement learning) open unexplored opportunities for tackling challenges or innovating radically.
The explosive nature of data makes it highly compelling for an organization to work with, but it can be equally devastating when it is not trustworthy or used in the wrong way. Hence, Thriving on ‘good’ Data is at the bottom of Maslow’s pyramid for Technology Businesses, and Thriving on Data ‘for good’ is at the very top, where the organization is activating data to fulfill its purpose in society.
Thriving on Data
If data is the corporate asset, treat it as such – by deeply understanding its sources and mastering all ways of the enterprise to leverage it. Learn more
A lack of specialized skills, the need to leverage data close to the business – and some powerful AI – are igniting the self-service data revolution. Learn more
3. Good Taimes
AI solutions require privacy, security, fairness, transparency, ‘explainability’, auditability and ethics to hit success – with the very best AI radiating the company purpose. Learn more
If the organization is distributed and data is everywhere, it is best to manage data in a federative way – balancing local ownership and a central platform drive. Learn more
Challenge everything you’ve tried so far with analytics and algorithms, AI brings alternative, awesome ways to solve problems. Learn more