Disruptive technologies such as hand-held devices, cloud computing and social media are rattling the foundations upon which traditional businesses are built. Enterprise customers have grown smarter at ensuring the latest technological trends work in their favor. Everyone is trying to zero in on their core competencies by employing commodity services to run their business.

Likewise, enterprise application vendors need to zero in on their core competencies and enhance more value to the businesses of their clientele by leveraging standards-based commodity services, such as IaaS and PaaS, provided by leaders in those segments (e.g. Amazon EC2, Google Cloud Platform etc.).

What else enterprises need to do is learn to adopt new and emerging technologies such as cloud, utility and social computing to build on them to penetrate new market avenues.

New small and medium-sized entrants into the market are constantly challenging enterprises given their ability to rapidly turnaround and address the requirements of the customers in a cost-effective manner. Additionally, these new advancements also affect how enterprises create, deploy, and manage solutions and applications. If you take the example of Force.com, for instance, you find that it’s a common war zone for enterprise application vendors to furnish SME markets with their applications, with the new entrants mostly having an edge.

Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”

Not getting any hits on your dating profile? It doesn’t mean you’re a loser. There are literally millions of computer dating profiles out there and dozens of free and paid websites to find a date. A dating profile is kind of like a resume for your personal life. To succeed, you have to learn a few tricks to stand out.

Tips to Improve Your Computer Dating Profile

1.  Make a Good First Impression

Filling out an online dating profile can be a daunting task. When you’re faced with all that blank space to write about yourself, it gets intimidating. The best way to improve your computer dating odds is to make a good first impression. Start with a good opening line such as a favorite quote or a personal motto you can easy build on.

Since its foundation, HSG has been a leader in Business Rule Management Systems Training and Consulting services by way of the Blaze Advisor Rule Engine.  Over the years we have provided such services to many of the worlds largest corporations and government institutions whose respective backgrounds include credit card processing, banking, insurance, health and medicine and more, much more.  Such training and consulting services have included:

Create a wrapper object model in either Java, .NET or XML

Identify and catalog business rules

Develop a rule architecture within Blaze Advisor that isolates rule repositories as they relate to functionality and corporate policies

Configure, develop and implement a variety of interfaces to the rule engine from disparate systems ranging from mainframe applications written in Cobol to UNIX/Windows applications using Enterprise Java Beans, Windows Services, Web Services, Fat Clients, Java Messaging Services and Web Applications.

Review and update code to boost efficiency either by way of

    Removing functions calls within conditional statements

    Ensuring that database calls are essential or can be rearchitected in some other manner

    Employing the rete algorithm where necessary

    Paring down extensively large class models

    Deploying such appliations in multi-threaded systems

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Call us if you:

    are in need of Blaze Advisor Expertise
    are developing SMEs in Blaze
    want to speak directly with an expert (no placement agencies)
    want an affordable alternative to FICO
    want to work with an industry leader