As data science has taken center stage in a lot of organizations, many are relearning what they’ve already known – that dry, mathematical calculations don’t inspire and don’t stick. It’s the story that matters. In this second of a two-part blog series, we look at some best practices for data storytelling and how Qlik analytics can help.
As data science has taken center stage in a lot of organizations, many are relearning what they’ve already known – that dry, mathematical calculations don’t inspire and don’t stick. It’s the story that matters. In this first of a two-part blog series, we look at the history and neuroscience of storytelling and how it can help us understand data at a more human level.
Qlik has performed extremely well in peer-based reviews over the years, but one peer-based review organization which sometimes flies under the radar is G2. They shouldn’t - G2 has been around for a decade and published over 1.7 million reviews covering thousands of software products.
If there is a job synonymous with numbers and data, it’s finance and accounting so you’d be forgiven for thinking that those working in these roles are, by extension, data literate – with the ability to read, work with, analyze and communicate with data. In fact, this is the lie. There is a very big difference between reporting on data and taking informed action using the insights data can provide.
Workforces across the world have changed rapidly over the past couple of years thanks, in large part, to two major trends. The first is the rapid digitization and transformation of service offerings during the pandemic. As our working lives shifted from office-based to online-based, the services that enable employees to do their jobs had to follow the same pattern. And with working practices predicted to remain a hybrid mix of office and remote-based, these changes are here to stay.
Qlik enhances analytics exploration in Snowflake by launching Direct Query, a new capability that allows Qlik Sense applications and dashboards to query Snowflake directly using SQL pushdown.
I recently sat down with CFODive to discuss the importance of modern financial analytics in transforming the way financial leaders and their organizations operate – a topic that is only becoming increasingly prominent. Business strategies have had to rapidly adjust to address market volatility, consumer trends, and unpredictable world events. These dynamics have forced finance teams to rethink how they are using data and analytics and take a more modern approach.
Finance has been at the forefront of enterprise analytics for decades. Over the years, these analytics have evolved from reactive, descriptive analytics related to financial performance, treasury holdings, and inventory management to predictive and prescriptive analytics for risk, credit, and financial business modeling.
You’ve heard the saying “if you do what you love, you’ll never work a day in your life,” right? Well, I hate to say it, but that’s me. I never dreamed that I would wind up in a field that combined all of my interests, but somehow that happened. Through my research at the MIT Media Lab I get to apply my legal and social sciences background to human-robot interaction. Which yes, does mean that I mostly get to play with robots all day.
To this point, AI has been applied to augment analytics in a somewhat bifurcated fashion. On one hand, we have seen natural language support the business consumer that requires simple answers to known questions, helping them quickly take action. And, on the other, AI helps content authors and BI developers auto-suggest charts and automate data preparation, improving efficiency and reducing manual workloads. But, there’s a gap, and the value is huge.
