In a previous post, we introduced a process and methodology for making data-informed decisions. The process defines the steps that should be systematically followed. The methodology outlines what needs to be done in each step of the process, including how and why to do it.In this post, we will add in the final piece of the puzzle: the skills that are needed to allow organizations to follow the process and apply the methodology accurately and successfully.
The hype around AI is deafening, creating a noisy and confused environment. Organizations are looking for clarity on the role and application of AI in bringing more value to data, and for help avoiding the pitfalls that surround so many of these projects. These pitfalls include error-prone decision making and unintended consequences based on flawed data or “black box” algorithms, and gimmicky approaches including simple search and bolt-on AI that overpromise and under deliver. We outline what you need to know as you think through these challenges in our new ebook, “Beyond the Hype: How to Get Real Value from AI in Analytics.”
Do you remember the time you wanted to learn to play an instrument or take on a new sport? The excitement and anticipation of acquiring the new skill that creates a buzz in your stomach. I remember that feeling when I decided to race a triathlon.
If you’ve been watching the data market for any length of time you can’t have failed to notice that there’s a shift underway. The line between data lakes and data warehouses is getting noticeably blurry. Not surprisingly Microsoft just announced Azure Synapse Analytics at it’s Ignite conference that is leading that charge.
According to positive psychologist Mihály Csíkszentmihályi, what I
described with this skiing experience is known as flow state, defined as an
“optimal state of consciousness where we feel our best and perform our best.”
Csíkszentmihályi, who popularized the term in his 1990 book, notes the mental
state of flow involves “Being completely involved in an activity for its own
sake. The ego falls away. Time flies. Every action, movement, and thought
follows inevitably from the previous one, like playing jazz. Your whole being
is involved, and you’re using your skills to the utmost.”
In my last blog post, I spoke about a series of articles I will be doing with regards to the world of data and analytics, specifically looking at the 4-levels of analytics, and data and analytical strategy. In this post, we are going to dip our toes into the world of data and analytical strategy, specifically looking at its importance and tie to the world of data literacy.
Netflix’s The Great Hack has exposed the significant dearth in public understanding around the way that many companies and governments are using our data. Over the past decade, our ever-more connected lives have resulted in an explosion of data, but it is only in recent years that we’ve seen discussion around its use by corporate and governmental organizations brought into the public consciousness.
As we embark on serving our users in the 3rd generation of BI, we are working to enable businesses with capabilities to leverage AI and machine-learning algorithms within their decision-making process. Staying true to our vision for the next generation of BI, we are empowering employees from all spheres of the organization to access the most powerful analytics and insights.
For the first time in human history, we have access to the second-by-second creation of vast quantities of information from nearly every activity of human life. It’s a tectonic shift that’s transforming human society. And among the myriad impacts is an important one for every business: the shift in data users’ expectations. In the same way that the advent of smartphones triggered expectations of access and convenience, the explosion in data volume is now creating expectations of availability, speed, and readiness. The scalability of the internet of things (IoT), AI in the data center, and software-embedded machine learning are together generating an ever-growing demand in the enterprise for immediate, trusted, analytics-ready data from every source possible.