Data Artisan's

I like the term "Data Artisans"

George Mathew, president and COO of Alteryx, predicts one of the hottest jobs in the future will be the "data artisan," a hybrid role that mixes data analysis with business savvy. "Data artisans will be asked to pull from structured and unstructured sources to drive the most important decisions within an organization -- like where it should open its next retail location, whether to pursue a new market, and which products to push,"

There's a lot of talk about big Data! and data artisans will be the new breed of thinkers that are strategically going to make management revolution by utilizing the technology around them. Though these are fancy new terms and not new for manager's who have always used data driven approach, yet the software tools that are available have grown leaps and bounds. These tools allow Data Artisans for handling large volume's of data, along with speed at which this data is being collected in real time to make substantial decisive actions.

A recent article on Big Data in HBR summarizes that data driven decision are better decisions as it enables the manager to look at evidence rather than intuition. It comes easy for companies that were born digital like Amazon and Google who are masters on big data. To know buying behavior and having key insights on what a consumer thinks that affects the bottom line changes the very fate/profits and direction of your organization.

Some key points I want to record here:

1) Big Data does not replace insight
2) Big Data does not replace vision
3) Big Data cannot replace value but can definitely help in understanding, inferring how our people are doing on core value.
4) Big Data coupled with insight will give you an edge in speed of decision making.
5) Big Data coupled with insight will help you minimize cost by allowing you to refocus your strategies.
6) Big Data has several kinds of information descriptive that may be (financial, demographic, pschyographic and social data), it could be Behavioral (response rates, likes, dislikes, activity, purchase rates etc etc. and finally Attitudinal (loyalty, satisfaction,  behavioral affinities etc.)
7) Tools that can close the loop on feedback and refine complex information in each of the mentioned areas in point #6 in simplistic manner (specially visually) help communicate and further your Organization better.

Thoughts,

Sam Kurien

CIO Metrics

CIO's today wrestle with more than just technology and the market demands that today's CIO's  be more than the technologist and the evangelist they are called to be. In fact they are more like CEO's in the sense they need to have keen interest in the business needs, future strategies and be an active part of ensuring that critical information churned out by techology support delivers timely info to the ground workers, people in field and empower the management/stakeholders of the organization. This is true for this role in Profit as well as Non-profit worlds.

The most important things that the mordern CIO must do is quickly identify business customers (internal and external)  to make sure the priorities are classifed and fit first within the IT operational plan and the strategic plan of the organization. Working with IT steering and governance committees,  trimming lists of the systems involved and streamlining applications and ensuring qaulity and efficiency are the outputs with a good reporting plan in place.

The second order of business would to standardize communications so key customers, stakeholders and field gets monthly and quarterly updates in regular fashion along with the insights of how the information can be used and what they really mean.

Thirdly defining a portfolio management process for evaluating and prioritizing projects across the enterprise that will have to meet organziational goals, objectives and be part of the strategic vision of the leadership. Key areas that fall into this broad area are that effect mainly the customer relationship management, communication management, events management and project management. 

Along with the primary three I know infrastructure managment (99.99% up time), application success, data and events all take considerable time in the life of the CIO's role, the success of the CIO then will be judged in the ability to manage expectations, supply & demand and be the chief neogtiatior of/for customer satisfaction while continually focussing on th big picture of projects in the bigger vision and calling of the orgnaization.

Thoughts,

Sam Kurien

Statistical Thinking - Regression?

Regress means to go backward. How did the term regression come to be? Sir Francis Glaton (1822-1911)  was the first one to apply regression techniques on biological and psychological data. He observed data in heights of tall parents and found that often taller than average parents tended to have children who were also taller than average but not as tall as their parents. Galton called this fact "regression" toward the mean and the name came to be applied to statistical method.

A regression line is very important measure in research then because a regression line summarizes the relationship between independent and dependent variables. as in specific settings one of the variables helps explaining or predicting the other.

Why am I relating this here? I remember a year I talked about visual mapping with mind map, certainly our brain does not always think in linear fashion, thoughts, ideas projects, to do's, likes, dislikes, desires, passions, etc are all over the place. So the question is while amassing information on architecture can you take your visual mindmap and plot out a linear regression?

Thoughts?

Sam

Fit Analysis- Is it Business ‘Fit’ or Technical ‘Fit’?

Enterprise Architects/IT Teams along with their business counterparts struggle with the idea of how to effectively engage the senior leadership regarding an application system that clearly does not fall in architectural/technology standard or simply the business standard. Recently talking to senior leader in our company his suggestion was – IT seems to have too much time on its hands to do a “What If” analysis. The comment didn’t catch me by surprise as the individual has hard time understanding not just technical but the business side of things too. A simple technique called FIT analysis can help in preparing a presentation for the senior leadership. The objectives mainly revolve around convincing the management about validating the IT road map for an application, make a strategic decision for better IT alignment and better maintenance of your application portfolios.

In Fit Analysis the first step is to always answer the question what is the business requirement or need that is trying to be met or in other words what is the problem we are trying to solve if its initiation of a project. Answering this question is an iterative task and covers more than one point on the vertical axis. The next step is to answer the question how the relevant business requirement fits or meets the technology standards in place, or if the organization is in transition towards a IT alignment roadmap the iterative process is carried out in asking the questions how “technical fit” is the application going to be. The result can be mapped out in a matrix quadrant with x-y axis.The result is four quadrants which are identified as “A” through “D.”

Quadrant A: High Business Fit but Low Technical Fit.

Projects or applications that fall into high business fit and low technical fit Quadrant A have a strong case from the business front but weak support or problerms in implementing on the technical front a good example here is at work we have a mission critical membership application piece written seven years ago, time to update it has long gone past but the lethargy of the management to change it or put substantial effort to revamp it lacks. On the other hand the code base is hard to maintain, is older technology and don’t meet upgrading or current IT standards. Sometimes an application or project may have a high business fit because the application owner or the project initiator has power of say or decision making but in reality the application may have a low business fit with other corporate strategies or a low technology fit that hinders standards alignment.

Quadrant B: Low Business Fit and Low Technical Fit.

Sometimes applications and projects fall into a category where it is a low business fit and a low technical fit. At work we have an application that does not have a monetary value or even a perceived benefit value, yet time and resources are spend sometimes behind these small applications. Support of replacing such systems in an organization is strong but again it hinges on who the application or program owner is. Traditionally the application remains without any measurement of the perceived benefit.

Quadrant C: Low Business Fit and High Technical Fit.

Applications and projects that fall into this quadrant do not have a strong business support but have a strong IT support because of its meeting the IT alignment standards. When this happens it becomes imperative for IT to do more detailed  "what if" analysis of how it affects the business ROI and find substantial reasons as to how it plays into the strategic positioning of the benefits matrix in the organization. The important thing here for enterprise architects is also to show how other functional requirements can be added or scaled to make it more relevant or congruent with being business fit.

Quadrant D: High Business Fit and High Technical Fit.

Applications and projects that are mapped into this quadrant typically have strong support from both the business and technology. This quadrant is the most comfortable quadrant for the EA. The EA does not have to worry convincing the stakeholders for undertaking such projects as most of the times they are mission critical.

Effectiveness of the Tool

This tool is most effective when this exercise is carried out in partnership with stakeholders or application owners most of the time senior leadership. The management is asked to plot or give their take on Business Fit and Technical Fit and rationale or reports they want to generate. The IT architect takes this rationale and along with the CIO  story boards the technical side and comes up with a model of how optimally can this application move towards being business fit and technical fit keeping IT alignment in mind. The plotted data will give how close the points fall to Quadrant D and then decisions are to be made are we going to go for it or not. Or go for it keeping strategic advantage in mind.

Thoughts,

Sam Kurien

Six Important Steps For Data Governance

Data is the most valuabe corporate asset and protecting that asset is primarily a management responsibility. Data has become the raw material or information taht ensures the continuation of a buisness and hence data governance is a strategic function. Another reason for protecting that data is also profits as well as non-profits have found that it helps also in the competitive advantage to add value to all the services and product offerings in their stable. Without going into advantages of mining that data for strategic business value additions I want to delve on the steps that a profit or a non-profit may want undertake in protecting this important resource.

1. Get a Governnace department going or identifying the individual that will be responsible for all data governance functions. A strong leadership in this area makes sure that value of data is conveyed to all quarters of the organization from top down. This person is reponsible not only about conveying that value but implementing stewardship policies that govern this important asset.

2. Identifying the redundancies and cutting the cost. The second step may be identifying the redundancies of data that comes in and flow through and churns out into information for decision making or value addition. If redundancies can be identified and eliminated that will bring costs down in maintaining that data. Focus areas will be resulting in more ease of accessing and storing of information and operational efficiency among staff.

3. Calculate the value of data in terms of its relation to your P&L. This is a metric function that the governance guy is responsible for. The value of data just like any other corporate asset changes over time based upon internal and external (market) conditions. This value is calculated based on the cost of IT services internal users pay for and value generated by revenues in terms of support or valu-addition.

4. Calculating Risk is next important step because it is an indicator how data may be compromised, loses it value in the future or losing its relevance. The strategies in this step involved combing the previous two steps in knowing what are internal operational inefficiences as well as exteranl operational threats that may be a risk for the data you are governning. This step involves also in calcualting the probability of risk and management of risk in terms of using tools of business intelligence, analyzing trends, past envents forecasting and overall policy management.

5. Implementation of Controls. This step involves the operational controls that you put in place for the stewardship of data. No one is excluded every corporate citizen internally is responsible and answerable to these policies. Practice often states that policy making at the top level tend to break their own rules of data governance controls and hence set themselves up for a fall. Controls should be routinely evaluaed in processes, process flows, information gathring and dissemination and if creating a bottleneck review and change over.

6. Finally Moniotring the efficiency of your controls .The last step is overlapped by step 5 in the sense so much of data governance is more a organizatioal response behavior that boils down to individual following the policies of data stewarship. Monitoring the control in place is thus more than just reviewing of the controls, it revewing of the people and expressing corrective measures in behavioral changes. This is where data governance becomes more that about data security, compliance and managing or assessing or risk. It is a compsoite discipline that bridges organizational functions and starategic management to ensure organizations own continuity in the marketplace.

Thoughts on important steps for successful data governance.

Sam Kurien