Today there is a strong move towards “Consumerization of BI” as business users are demand the same speed, dashboards and ease of use from their workplace applications as their at-home software.
This is a major catalyst for change. In every major corporation there is a renewed push to industrialize and improve data visualization.
The challenge is not in procuring the next greatest tool or platform but how to organize the people, process and assets effectively to create value, reduce training and support costs.
In other words, how to facilitate and create a flexible operating model for data mining and visualization delivery that provides discipline at the core while giving the business the agility that they need to make decisions or meet client needs?
Decision making is a core business activity that requires facts and insights. Slow, rigid systems are no longer useful enough for sales, marketing and other business users or even IT teams that support them. Competitive pressures and new sources of data are creating new requirements. Users are demanding the ability to answer their questions quickly and easily.
So the new target state is to empower business users along the Discover, Decide and Do lifecycle:
- Discover new insights by rapidly accessing and interrogating data in ways that fit how people naturally think and ask questions.
- Decide on best actions by publishing dashboards, collaborating with others, discussing insights and persuading others through data presented in an interactive application (“app”) rather than in a static view.
- Do what is best at each decision point with confidence, based on the consensus that develops when new data is aggregated and explored with multiple associations and different points of view. Teams can take action more rapidly and move projects forward more effectively when everyone understands the data underlying decisions.
The challenge for business users is data discovery and ease-of-use. They want to focus on aggregating and visualization. They want the interactive ability to quickly change filters and query conditions.
The challenge for infrastructure and application teams in every corporation is to deliver new easy-to-use platforms to their business partners quickly and consistently while maintaining governance and control.
To meet both sets of requirements, best practice firms are creating Data Mining and Visualization Competency Center or Centers of Excellence (DV-CoE) to ensure that the people, process and technology investments are not duplicated and addressed in a way that maximizes ROI and enhances IT-Business partnership. I have seen many cases where not having a proper structure leads to sub-optimal results. Read more
More data + Better models + More accurate metrics + Better approaches & architectures = Lots of room for improvement!
It’s amazing to watch how quickly the data engineering / analytics/ reporting/ modeling/ visualization toolset is evolving in the BI ecosytem. There are clearly massive foundational shifts taking place around big data. I am not sure how large conventional Fortune 500 firms can innovate and keep up with what’s going on. I have run into CIOs who have not heard of Hadoop in some cases.
It’s also fascinating to see how data-driven “bleeding” edge firms like NetFlix are pushing the envelope. NetFlix is clearly reinventing Television and targeting 60 million to 90 million potential subs in the US market alone. Binge-watching, cord-cutting are now part of our everyday lingo. What most people don’t realize is how data-driven Netflix is…. from “giving viewers what they want” to “leveraging data mining to boost subscriber base”.
Viewing -> Improved Personalization -> Better Experience is the virtuous circle.
Here is a glimpse at how their BI landscape has evolved in the past five years as they integrate 5 million to 6 million net adds for several years now. The figures are from a presentation by Blake Irvine, Manager Data Science and Engineering.
BI tools @ NetFlix pre-Hadoop
New Technologies | New Possibilities
As a C-level executive, it’s becoming clear to me that NoSQL databases and Machine Learning toolsets like Spark are going to play an increasingly big role in data-driven business models, low-latency architecture & rapid application development (projects that can be done in 8-12 weeks not years).
The best practice firms are making this technology shift as decreasing storage costs have led to an explosion of big data. Commodity cluster software, like Hadoop, has made it 10-20x cheaper to store large datasets.
After spending two days at the leading NoSQL provider MongoDB World event in NYC, I was pleasantly surprised to see the amount of innovation and size of user community around document centric databases like MongoDB.
Data Driven Insight Economy
It doesn’t take genius to realize that data driven business models, high volume data feeds, mobile first customer engagement, and cloud are creating new distributed database requirements. Today’s modern online and mobile applications need continuous availability, cost effective scalability and high-speed analytics to deliver an engaging customer experience.
We know instinctively that there is value in all the data being captured in the world around out…no question is no longer “if there is value” but “how to extract that value and apply it to the business to make a difference”.
Legacy relational databases fail to meet the requirements of digital and online applications for the following reasons:
“Have we got a girl for you” Some very sophisticated predictive analytics are powering the online dating or hookup world. A lot of innovation is taking place around real-time, geo-location based matching services.
Take for Match.com which debuted its online dating first site in the U.S. in April 1995. Today, the Match.com brand hosts sites in 24 countries, in fifteen different languages spanning five continents. Match.com offers an interactive way for singles to meet other singles with whom they might otherwise never cross paths.
How to model and predict human attraction? Match.com is powered by Synapse algorithm. Synapse learns about its users in ways similar to sites like Amazon, Neflix, and Pandora to recommend new products, movies, or songs based on a user’s preferences.
Match.com uses Chemistry.com to do personalized surveys and get detailed preference data. But when it comes to matching people based on their potential love and mutual attraction, however, analytics get significantly more complex when you are attempting to predict mutual match… the person A is a potential match for person B…. but with high probability that person B is also interested in person A. Read more
If you’re an executive, manager, or team leader, one of your toughest responsibilities is managing and organizing your analytics initiative. Data-driven business models are not a nice-to-have but a need-to-have capability today.
The days of business as usual are over. Data generation costs are falling everyday. The cost of collection and storage is also falling. The speed of insight-to-action business requirement is increasing. Systems of Record, Systems of Engagement, Systems of Insight are being transformed with consumerization and digital.
With this tsunami of data and new applications, the bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven “sense-and-respond” Action. This slide from IBM’s Investor Briefing summarizes the data-driven transformation underway in most businesses.
Better/Faster/Cheaper Analytics Execution
Are you data-flooded, data-driven, data informed? Are you insight driven or hindsight driven? Are you a firm where executives claim – “Data is our competitive advantage.” Or sprout analogies like, “data is the new oil”.
The challenge I found in most companies is not dearth of vision… having a 100,000 ft view of the importance or value of data. Every executive can parrot the importance of data.
The challenge is the next step….so, how are you going to create new data products? How are you going to execute a data driven strategy? How are you going to monetize data assets? What are the right use cases to focus on? What platform is a good long-term bet? The devil is in these details.
Everyone is searching for new ways to turn data into $$$ (monetize data assets). Everyone is looking for new levers to extract value from data. But data is simply a means to an end. The end is not just more reports, dashboards, heatmaps, knowledge, or wisdom. The target is fact based decisions and actions. Another target is arming users to do data discovery and insight generation without involving IT teams…so called User-Driven Business Intelligence.
In other words, what is the use case that shapes the context for “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.” What are the right use cases for the emerging hybrid data ecosystem (with structured and unstructured data)?
Decision support needs better visualization. Scorecards, Dashboards, Heatmaps, Alerts, Management Reporting, Operations and Transactions Reporting are all enterprise example of data visualization outputs.
Some data visualization examples include:
- Data Scientist — uses “R”, a programming language used for statistical modeling, to understand traffic flows and congestion patterns and advise on options to improve travel times for Amazon.com Local delivery drivers.
- Pharmaceutical Sales Representative — uses QlikView on an iPad to access current industry sales trends and doctor prescription history while on a sales call with a busy physician.
- Healthcare Chief Medical Officer — uses Tableau Software to analyze all aspects of hospital performance including population management, emergency room effectiveness and Affordable Care Act compliance.
- Crime Analyst— uses Microstrategy to maintain a consolidated view of crime levels and optimize staffing allocations to dispatch police into high crime areas.
- Retail Store Manager — uses QlikView to analyze which products are selling best which impacts store assortments and which products get featured vs which ones get discontinued.
- Telecom Customer Service Agent — uses Spotfire to monitor call center statistics and how it translates into customer satisfaction and retention.
Bitcoin — the Internet currency, payment system and technology — is about the birth of a new “digital” monetary ecosystem. Like every innovation it creates new regulatory and compliance challenges. There is growing interest in knowing where the money has come from and at the same time the anonymity of bitcoin makes creating an data trail a tricky task, but it’s possible to say whether certain bitcoin addresses are involved in mining, or have been associated with gambling transactions.
More recently, nationally known merchants like Overstock.com, Zynga and the Sacramento Kings basketball team have begun to accept Bitcoin payments. Even political candidates are taking donations through the system. Worldwide transaction volume keeps growing, as does the number of Bitcoin users.
Bitcoin is built on some heavy and complex data-crunching. Like any ecosystem, it will have its share of winners and losers. The Bitcoin “Innovative Disrupters” are those that have the best odds at being winners. Read more
Do Bitcoin and other cryptocurrencies provide opportunities for innovative entrepreneurs to create real value? Bitcoin has proponents and naysayers. What is it really about?
The 18th century philosopher Voltaire, a proponent of the separation of church and state, is known to have said that the Holy Roman Empire was neither Holy, nor Roman nor an empire. We could say the same about Bitcoin as a virtual cryptocurrency.
Cryptology has an important role to play in Bitcoin, but that is not its defining feature. Currency is historically built on the recognition of national boundaries and their associated political constructs. Moreover, the words “crypto” and “fiat” are not opposites.