5 quick rules for charting – Chandoo.org

Good charting tips from Chandoo.org :

Read more here.

Eight Levels of Analytics

I came across a lovely article on levels of Analytics Maturity on SAS website. The 8 levels mentioned in the article are

Standard Reports

Ad Hoc Reports

Query Drilldown

Alerts

Statistical Analysis

Forecasting

Predictive Modelling

Optimization

I am yet to see maturity at anything more than a level 4 in most of the organizations that I know about, however I have been a part of Statistical Analysis and Forecasting in the BPO World. Click here to read the article, or here to download the pdf version.

Analysis and Analytics

In continuation with some definitions of Data Analytics and Analysis here, the following graphic came up after a small discussion of how and when the Analysis and Analytics components play a role.

Will write more about various methodologies and tools as I learn more!

Definition – Data Analytics

A detailed definition of Data Analytics from Search Data Management Definitions. Here is what it says :

Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics is distinguished from data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.

The science is generally divided into exploratory data analysis (EDA), where new features in the data are discovered, and confirmatory data analysis (CDA), where existing hypotheses are proven true or false. Qualitative data analysis (QDA) is used in the social sciences to draw conclusions from non-numerical data like words, photographs or video. In information technology, the term has a special meaning in the context of IT audits, when the controls for an organization’s information systems, operations and processes are examined. Data analysis is used to determine whether the systems in place effectively protect data, operate efficiently and succeed in accomplishing an organization’s overall goals.

7 Dirty Little Truths About Metrics from DITY

Looks like today is a number day! The previous post was with the number 6 and this one with the number 7.

Hang Marquis in this weekly newsletter from DITY (Do IT Yourself) writes some truths about Metrics followed by a simple checklist to create metrics that matter.

Click here to read the article or here to download a PDF. For my own benefit, here is the checklist that he has written in his article:

Align with Vital Business Functions. Regardless of the IT activity, you need to make sure your metrics tells you something about the VBF that depends on what you are measuring.

Keep it simple. A common problem manager fault is overloading a metric. That is, trying to get a single metric to report more than one thing. If you want to track more than one thing, create a metric for each. Keep the metric simple and easy to understand. If it is too hard to determine the metrics, people often fake the data or the entire report.

Good enough is perfect. Do not waste time polishing your metrics. Instead, select metrics that are easy to track, and easy to understand. Complicated or overloaded metrics often require excessive work, usually confuse people, and do not get used. Use a tool like the Goal Question Metric (GQM) model to clarify your metrics.

Use metrics as indicators. Key Performance Indicators are metrics! A KPI does not troubleshoot anything, but rather the KPI indicates something is amiss. A KPI normally does not track or show work done. Satisfying several KPI normally means satisfying the related CSF. The KPI is an indicator, a metric designed to alert you that CSF attainment might be in jeopardy, that is all. A good metric (KPI) is just an excellent indicator of the likelihood of attaining a CSF.

A few good metrics. Too many metrics, even if they are effective, can overwhelm a team. For any processes 3 to 6 CSFs are usually all that is required. Each CSF might have 1 to 3 KPI. This means most teams and individuals might have just 2-5 metrics related to their activities or process. Any more and either the metrics won’t get reported, or the data gets faked. Too many metrics transforms an organization into a reporting factory — focusing on the wrong things for the wrong reasons. In either case, the usefulness of the metric is compromised.

Beware the trap of metrics. Failure to follow these guidelines invariably results in process problems. Look around your current organization.

And talking about number 8, here is a link to another post that talks about 8 features of a successful real time dashboard.