Decision Management Solution Blog
A Q&A on the role of decision modeling in analytics
Jim Powell just published a Q&A with me on Using Decision Management to Maximize Analytics. We covered a bunch of things:
- What, exactly,is decision management and what does it have to do with advanced analytics?
- What’s the history of decision management – where did it come from and does it work?
- What kind of decisions are we talking about here?
- These are not the decisions people usually focus on with analytics so how can you identify suitable decisions?
- Why do you need a new way to describe the analytic requirements of these decisions?
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Alteryx Inspire 14 Influencer Summit – A panel of experts
The summit continued with a group of newly certified Alteryx Analytic Certified Experts – ACEs. As always with a panel I will just try and capture a few nuggets in no particular order or structure:
- The combination of Tableau and Alteryx, in geospatial analysis for instance, is more powerful than either independently. In particular Tableau benefits from the data blending capabilities of Alteryx.
- “Win” on day zero with an ability to get benefits from Alteryx almost immediately – creating something valuable without a big upfront cost. It’s got a lot of capabilities but it is focused and easy to learn.
- Iteration is central – can get started quickly with a simple solution and then iterate and improve it quickly. A more agile, iterative environment.
- The users a
Alteryx Inspire 14 Influencer Summit – Partner Strategy
Next up at the Alteryx Influencer Summit a discussion of their partner program and strategy. This strategy has three parts:
- Enable technology partner ecosystem and engage their user base
- Extensibility partners like Qlik, Tableau, Revolution, Salesforce
- Big Data infrastructure partners like Teradata, cloudera, Pivotal, Hortonworks etc.
- Some of these are go to market partners – Qlik, Tableau, Datasift and Revolution – while some have joint account work and some are more just about connectivity.
- Land and expand through VARS – value added resellers
VARs used to be very data-centric, bringing industry-specific data or expertise to the conversation, but increasingly these VARs are also VARs for Tableau and Qlik. - Create large opportunities through SIs and analytic consultants
Both boutique specialty analytic consulting firms and the analytic divisions of big SIs.
This partner
Alteryx Inspire 14 Influencer Summit – Growth Strategy
Next up at the Alteryx Influencer Summit was a discussion of the overall growth strategy from Rick Schultz and Paul Evans (Marketing and Sales). Most of this is under NDA, as you would expect, but Alteryx was willing to discuss how they have been improving their customer acquisition approach:
- A laser focus on the business analyst as their #1 audience to create first users and evangelists.
- A cleaner message around an intuitive workflow for data blending and advanced analytics – deeper business insight in hours not weeks.
- Everything about the web experience was redesigned to focus on this core user and what they might want to do/see to be persuaded
- A big focus on the Tableau community thanks to the heavy overlap between the two tools’ audiences.
- They offered lowered pricing for the designer and increased the transparency of the pricing model.
They have expanded from their initial set of industries (reta
Webinar: 6 Opportunities for Business Improvement with Decision Management
I am giving a webinar at 9am Pacific on June 25 – “6 Opportunities for Business Improvement with Decision Management”
What opportunities are you looking for to improve your business performance? In this webinar you will learn six opportunities that are readily available when you adopt a decision management approach to business rules and predictive analytics. These are opportunities to:
- Simplify business processes and so reduce costs and increase business agility by externalizing decisions from them.
- Increase straight through processing and so reduce costs by applying decision management technologies to automate decisions.
- Increase the speed and accuracy of tactical and managerial decision-making through decision modeling and improved decision support systems.
- Use analytics to eliminate “one size doesn’t really fi
IBM Big Data & Analytics: Internet of Things and Predictive Maintenance
Last breakout session at the IBM Big Data and Analytics day in on the Internet of Things and Predictive Asset Maintenance. The IoT is creating new opportunities in all sorts of industries from route optimization to border control, power management in buildings to managing ATM infrastructure. Increasingly organizations want to respond in real-time to things being detected by sensors. The IoT helps with the Sense and Build elements of the Sense-Build-Decide-Act cycle, helping you establish that something has happened and the context of what happened. As more IoT devices have actuators as well as sensors it also enables automated Act once a decision is made.
For IoT IBM is leveraging its Bluemix PaaS offerings  – both IBM components and partner components like geolocation services. They have created an IoT starter kit with Informix for data capture, data connectors, and Node Red for stringing together IoT devices with Javascript. The kit allows companies to connect devices
IBM Big Data & Analytics: Predictive Customer Intelligence
IBM recently introduced a Predictive Customer Intelligence solution. Like the counter fraud solution earlier, this is one of IBM’s multi-product and services signature solutions. The focus of this solution is the increasing need to be truly customer-centric, focusing marketing on individual customers not on segments or campaigns, not on pre-existing mental models or ideas. This matters because customers are changing with 75% disbelieving ads, only 8% thinking that organizations are providing a superior experience for instance. This is challenging due to multiple channels, incomplete information, problems delivering personalization on every channel and doing all this in real-time. Success requires putting customers at the center.
IBM believes that by co
IBM Big Data & Analytics: Analytics and Fighting Fraud
An update next on IBM’s solution for counter-fraud.  For IBM fraud involves an intentional act that misrepresents and is both illegal and designed for financial gain. They further divide this into organized and opportunistic fraudsters for whom different kind of detection is required. Fraud is growing problem because:
- Fraud schemes are increasingly sophisticated with organized crime moving it it due to both opportunity and lower risk of violence/arrest
- Fraud is no longer acceptable as a cost of doing business – regulatory environment is tigher for instance
- Consumers expect less fraud and are increasingly cranky about it, reporting it more readily for instance.
Schemes are getting sophisticated involved hacks, networks of people globally and laundering all being used together such as one that changed ATM limits on some compromised accounts, used people in many countries to withdraw and launder millions of dollars.
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