A1 Super varied – some are saving/making billions, others are flailing. Some of the elemental stuff is sorta easy - like saving fuel costs by always turning right - but most 'human behaviour' is so complex makes analytics super hard.
A1. Most organisations are still data rich but insights poor, but now against a backdrop when data ecosystems continue to expand and become more diverse
A1. Different organizations have different sets of challenges — some easier than others. The key is finding the right combination of technology and internal skills to take data and turn it into positive business outcomes.
In 1987, economist Robert Solow declared, “You can see the computer age everywhere but in the productivity statistics.” However, it wasn’t until recently that they could truly realize the business value of data.
A1: The increased focus on #DataAnalytics and its success in extracting #value is leading to its acceptance. There are now formal Chief Data Officer titles and dedicated data analytics departments building products and providing oversight, governance and compliance.
A1: Many companies are somewhere in the middle on their #dataanalytics journey, but Gartner’s Data & Analytics maturity assessment of over 1k companies shows that not 1 industry scores a 3/5 for maturity. So there is a lot of opportunity for improvement.
Success is limited, with exponential increase in cost to capitalize deriving value from more data, Gartner has reported 60% of orgs unable to move analytics deployments to production, so that tells me the majority are floundering
A1: In my experience, successful analytics at scale has to be supported by a platform that enables the full data lifecycle — that is seamless and secure collaboration between everyone — across any hybrid or multi-cloud environments.
A1. Little bit of both, depends on the buy-in at the business-level on where they want to go, and a fundamental alignment across the C-suite on a vision of the future. Technology improvements have enabled some fantastic new use cases
A1: Companies that are stuck using BI tools built 15+ years ago are floundering. Data has evolved but traditional BI tools haven't...Few examples: modern tools need to support graph analytics, going beyond simple 2D plots & allow for effective collaboration and storytelling.
A1: The key is enabling a true hybrid data fabric powered by an open infrastructure that unifies all of your data and analytics. What people need is a secure platform that lets businesses do anything required of their data when they need it.
Analytical and actionable insights are leading to better business decisions for orgs. Findings show, there is a strong correlation between quality and speed of analytics, especially scale analytics vis-a-vis org. profits. #DataAnalytics#eWEEKchat@CIOStraightTalk
A1. But in reality, projects live or die by the adoption, engagement, and socialization of 'why' and 'where we're going' more than anything technology related.
A1 @JamesMaguire - A study by Harvard Business Review reported that 87% of organizations believed they would be more successful if frontline workers were empowered with data–yet only 20% of them had made moves to put data into the hands of those workers. #eweekchat
@RKs2cents Good call Radhika, this is making a big dent - hiring actual specialists, treating it like real science. This is important move away from sandboxes and playgrounds!
A1: Customers who are quite successful are usually ones who define goals, metrics, and outcomes in advance. Trying to boil the ocean because you have a lot of data usually leads to less successful outcomes because it's hard to execute and measure too much.
A1: We've found that companies that share a unified data structure, consistent core definitions of KPIs and attributes, and a unified customer profile are able to better respond to customer needs and overall trends.
As companies experience the shift, they are moving toward best-of-breed stacks and empowering people throughout their organization to have access and the tools needed to make more data-informed decisions. #eweekchat
ooo, I would like to subscribe to your newsletter!! FR, collaboration and sharing of patterns and process are empirically shown to improve business outcomes!
A1: The bottom line is that the technology used is half the battle. a well structured and coherent system for working with data is essential, but best used with the right structures of skills, people, and organizational aspects such as data fabrics and meshes that make it work.
