Analytics Engagement Framework

After completing a number of analytics engagements a common theme has emerged on how to systematically approach this problem. This…

After completing a number of analytics engagements a common theme has emerged on how to systematically approach this problem. This framework covers how to go from the desire for a business to be data driven to an analytics solution.

Most companies today are collecting large amounts of transactional, customer and employee related data. This data is stored in business systems such as Salesforce.com, SAP, Oracle, Teradata, Hadoop, etc. Data and analytics can help companies gain competitive advantage by leveraging this data and almost always generating additional revenue. These competitive gains are only possible if a solution is devised that continuously ingests company’s internal data-sets and produces timely actionable insights. The goal of such solutions is to incorporate data insights into day-to-day operations of the organization.

WORKFLOW

The diagram below shows the process and the checklists for each of sub-processes.

(Click figure to Zoom in)

To build these solutions this framework has been tested several times and incorporates many of the lessons learned over the years. The framework generally follows these six steps:

1. Understand Business Process: Work closely with client to understand business strategy and goals for data/analytics solutions. Typical goals can be as simple as bringing business process transparency to more complex goals such as enabling experimentation, customized experiences or automated decision making.

2. Form Analytics Strategy: The next step includes determining what Key Performance Indicators (KPIs) or metrics measure business success. During this step existing internal and external data-sets are analyzed to identify useful metrics and highlight gaps in data instrumentation. Ad-hoc analysis and data exploration during this step can lead to better KPIs. Lastly, during this step wire-frame designs are constructed and shared with client to get feedback on potential solutions. At this step decisions are also made about which features are ‘must haves’, ‘performance enhancers’ and ‘delighters’.

3. Design & Develop Solution: With a clear understanding of data-sources, business goals and KPIs, a solution architecture starts to take shape. Design and development of such solutions is done using one of several tools available. Typical tools used are discussed later in this post. While larger clients tend to want most features incorporated during this step, it is often best to limit the first version to the ‘must have’ features.

4. Deploy to Client: Deploying an analytical solution involves setting frequency of data collection, data validation, analysis and dashboard updates. Additionally, this is when clients get a first look at the end product. End product in this case can be a dashboard, report or even a detailed presentation. Using feedback from client, this is also a good opportunity to make any major required changes and evaluate if features meet client needs. Using the Kano model is an easy way to quantitatively understand the needs of your client.

5. Execute & Iterate: Once deployed, a cycle of execution, measurement and iteration starts. From the client’s perspective this cycle has four steps: (1) gather and sanitize data (2) update visualizations (3) perform analysis and (4) share with clients. At the end of each cycle analysts asses and iterate on the product. This is also a good opportunity to incorporate ‘performance enhancers’ and ‘delighter’ features.

6. Evaluate: After a few rounds of execution, analytics design product becomes operational. At this point analysts work with the client to evaluate analytics ROI and value added to the business.

TOOL FOR BUSINESS ANALYTICS

Tools used for analytic and data science projects can be separate topic on its own. However, given the relevance to this framework, some of today’s (2013/2014) tools could be highlighted as:

• Data Curation: SQL, Pig/Hive

• Data Discovery (ad-hoc analysis): R, Python, Matlab, Excel

• Dashboard Designs & Mockups: Balsamiq, MS Visio

• Data Processing & Product Prototyping: SAS, R

• Machine Learning: R, Python & Mahout

• Visualization & Dashboarding: Plotly, Tableau, QlikView

• Presentation & Reports: PowerPoint & PDFs

• Web A/B testing: Optimizely

MixPanel, Google Analytics and GoodData are also common at startups for web and mobile analytics. I have also seen the following used, but am personally not a big fan of them: MircoStrategy, Essbase, etc. There are several players in this space offering all kind of tool as shown in the diagram below:

Source: http://www.erikpenn.com/social-media/the-big-data-landscape/

A more detailed marketplace chart is available here.

DELIVERABLES

The most important deliverable is a product/dashboard/report that meets the needs of your client to run a data driven business. Each process gate can also have less structured deliverables as shown below:

Ideally, success criteria for each deliverable should also be defined for each step. For the over all project a common criteria applies “Timely Actionable Insights”. A solution that meets these three criterias ultimately can help move the business forward.

Originally published at ehasan.com.