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#ICYMI: This is part six (you can find part five here) of an eight-part series on the Department of Veterans Affairs microconsulting project, “Governance Models in Public and Private Sector.” Providing an overview of API governance to help the VA, “understand, with the intention to adopt, best practices from the private and public sector, specifically for prioritizing APIs to build, standards to which to build APIs, and making the APIs usable by external consumers.” Pulling together several years of research conducted by industry analyst API Evangelist, as well as phone interviews with API practitioners from large enterprise organizations who are implementing API governance on the ground across the public and private sector, conducted by Skylight Digital.

We’ve assembled this report to reflect the interview conversations we had with leaders from the space, helping provide a walkthrough of the types of roles and software architecture being employed to implement governance at large organizations. Then, we walk through governance as it pertains to identifying possible APIs, developing standards around the delivery of APIs, how organizations are moving APIs into production, as well as presenting them to their consumers. Wrapping up with an overview of formal API governance details, as well as an acknowledgment that most API governance is rarely ever a fully formed initiative at this point in time. Providing a narrative for API governance, with a wealth of bulleted elements that can be considered, and assembled in the service of helping govern the API efforts across any large enterprise. https://goo.gl/J7PgrN #DataIntegration #ML

#ICYMI: I picked up a little book called “Finance Basics” published by Harvard Business Review Press, for a short in-flight reading. This tiny book isn’t going to make someone a finance expert but I did find a few things useful for data scientists and business analysts whose background is not finance or economics. Data science is truly a multi-disciplinary area with people coming from many different background and areas of expertise, often with little to no exposure to finance and economics. So I am highlighting few things that can be valuable for anyone in the community.

Before going into those “basics” here’s what the book says about WHY understanding finance is necessary in the first place:

* To get a sense of which products/services are performing well and where, and which are not doing well.
* Are cost of production and marketing justified? Is there room for improvement?
* If the company decides to invest $X more on a product will it generate at least $X in revenue?
* How the company’s revenue, cost, profit and overall financial health look like looking into the future?

Anyone can Google to learn details, but here are my 4 (four) take aways that can make you feel good about finance even if you never took a finance course.

* Try to make sense of 3 major financial statements: income statement, balance sheet, cashflow statement. Income statement is where you get the famous “bottom line” of the company you work for.
* Can you prepare a budget for a project (or idea) that you are proposing? Learn at least some basics.

* Learn few things about benefit cost analysis if you are proposing a new idea or product. The following related terms may sound intimidating, but I promise you already know them from you everyday experience.

* Return on Investment (ROI)
* Benefit Cost Ratio (BCR)
* Payback Period
* Net Present Value (NPV),
* Internal Rate or Return (IRR)
* Break-even Analysis

* And finally, few other jargon that you might be interested in. Again I promise, most of them will sound familiar to you, nothing out of the world: Accounts payable (A/P); Accounts receivable (A/R); Assets; Asset turnover; Balance sheet; Book value; Break-even; Capital; Cost of capital; Debt; Dividend; Equity; Fixed and variable cost; Gross margin; Gross profit; Hurdle rate; Inventory; Liabilities; Valuation; Working capital.

About the author: An economist and data scientist with multi-disciplinary academic and professional training. Website: Data2Decision;Twitter: @DataEnthus https://goo.gl/4o4uAR #DataScience #Cloud