Analytics for Business

The increasing quantum of data in the enterprises and the willingness to turn this data into actionable content has led companies to turn towards business analytics. The form based applications which used to fill the database and the data which became the target for simple queries has now proved to be the source of all the excitement surrounding the interesting field of analytics and data sciences. Data management on one hand is an interesting science or a problem in its entirety, but is different from how this mountain of data can be used to extract insights about the business and how it can lead to customer acquisition and retention.

We do see tonnes of free or open data on the Web, and the increasing numbers of infographers and designers who create interesting visualizations using these has only increased in the last few years. Part of it is due to the Social boom which has made the mobile the biggest source of data generation. Twitter updates, Facebook , Flickr and Instagram photos etc have contributed immensely to this spurt in the ‘data-generation’ ; note that textual data along with other forms – namely photos, videos etc also have lot of ‘information’ embedded in them which needs to be suitably tapped. Coordination of multi-channel data(i.e from mobiles, chats, social, surveys, call-centers, in-person, in-house etc) is also an important topic in here where data from different sources are suitably assimilated and cleansed for further analysis and reporting.

Enterprises are mainly interested in the following facets when it comes to Business Intelligence:

• Knowing what you know 

This includes understanding any causal relationships, tracking progress, KPIs  etc. Basically, it means that you knew something was happening, but you want to better understand what made it happen or you want to make sure that a step taken by you has actually made something else happen – a.k.a causal relationships. The famous example of a Retail store moving the Dairy products section close to the Vegetables/Fruits section in a supermarket(and similarly rearranging different sections in the store)  and thereby increasing the sales is often cited example in this case (and there are many more).

• Knowing what you don’t know

This is where business intelligence tools offer immense value and is the focal point for any sales pitch. Can your tool find out what is happening in(or outside of ) your organization, which is not otherwise easily perceivable/reportable. Also, Data Discovery software/tools have lent a new paradigm in this sphere, where, as an analyst/executive, you simply start browsing the charts/data and with the drill downs you essentially figure out something which was previously unknown – i.e, you start from somewhere without an objective but during the due course of browsing(aka discovery) you stumble upon some very interesting facts or relationships.

Social is increasingly seeping into the enterprises too, and there is this complete new realm of Social Business Engineering and Social Relationship Management which has lent a unique perspective to the way customer relations can be handled. This has in-turn led to more data-spurt. But what seems to be lacking in this sphere is the ability to decipher this immense knowledge that is hidden in the data and turn it into 'actionable content'.

Analytics should be implemented in an enterprise in such a way that people in different stratums of the organization can extract value out of the deciphered content and make useful decisions. Though the data is the source of truth here, every stratum in the organization looks at the same data in a different way.

Let’s take an example : The mobile phone industry is growing exponentially, and you will find a basic mobile phone even with the kid next door. People prefer and choose the mobile models based on different criteria - usually their work/eductation/sex/age etc comes into play and also they have different uses and expectations out of it. And once they buy it, it is pretty common for them to post a review of the phone in any of the review sites or on twitter or blogs. Also, the sales data is captured in the company’s order management tools. So, if you see we have one form of unstructured data(namely reviews etc) and also the structured data which is captured in the order management tools.

Now, let’s role-play:

As a Product Manager,

I would be interested in knowing what the customers are talking about it – mainly with respect to the features, issues etc. And this can be obtained from Social platforms, surveys, etc.

As a VP of Sales,

I would be interested in knowing the sales numbers, sales by geographies etc etc. This is captured in the Order Management tools  - i.e, structured or relational data.

As Head of Product Marketing,

I would be interested in knowing the issues and also have to manage the reputation of the brand etc. Need to suitably channelize the opinions generated from social media in my new marketing campaigns. Also, my market research team needs to be suitably engaged in understanding my competitors’ strategy and also listen to what the customers are speaking about them. The data comes from Social platforms – i.e, unstructured data mostly; and also from call-centers and surveys etc.

As you see, it’s the combination of structured and unstructured data analytics that will give the different executives in an organization into what they want to see with respect to their job roles. Integrated Analytics is no longer a fancy word, but a requirement for a holistic approach towards gaining insights from data.

The so called ‘data explosion’ did not happen overnight; the data was just piling up day after day for months in enterprises. The business motivation to actually use this data for better decision making and gather actionable insights coupled with the development of interesting business intelligence software and data sciences tool, made the case for investing in BI in Enterprises a valid case. And once the CIOs started investing in BI tools, they could see the actual RoI(Return-on-Investment). The ‘software’ factor was soon forgotten, and the business leaders and the other segments in an organization were actually keen on the intelligence that could be gathered from historical and real time data. There is/was a direct positive impact on the revenues for those companies which leveraged Business Intelligence tools – not to mention the extremely interesting insights that they could get by diving deep into their datawarehouses/datamarts.

Oracle is positioned nicely in the analytics industry with its extremely powerful Business Analytics Solutions which tackles almost the challenges listed above. The solutions offer analyzing both structured and unstructured data and the tag line of 'See More, Act Faster' fits in perfectly and carries the philosophy of the various products offerings.

The main Business Analytics Solutions offered by Oracle include:

• Enterprise Performance Management

1. Oracle Strategy Management
2. Oracle Planning, Budgeting and Forecasting
3. Oracle Profitability and Cost Management
4. Oracle Financial Close and Reporting
5. ...and many more..

• Business Intelligence
1. Oracle Business Intelligence Tools and Technology
2. Oracle Business Intelligence for Analyzing Big Data
3. Oracle Real Time Decisions
4. Oracle Endeca Information Discovery
5. ...and many more...

• Engineered Systems
•  Oracle Exalytics In-Memory Machine

You can  check the entire portfolio of Oracle's BI products along with other details in Oracle Business Analytics Page. Oracle Essbase and TimesTen are related products which are often used in the BI/EPM software stack - we will cover these in later posts.

In the future posts, we will dive into the specifics of various Oracle Business Intelligence offerings and also look at some interesting features, often faced challenges/issues, technical tid-bits etc. Keep tuned in :)