Transforming Data for AnalyticsSubmitted by admin on Tue, 2016-03-29 19:37
Each day, social media networks record the interactions of millions. Sensors measure our changing weather. At sports events, fans collect reams of statistics on athletes' performance.
Data is out there, reflecting all aspects of life. But it's just pictures, numbers, and facts - unless it can be interpreted, and put to dynamic use. That's where analytics comes in.
What is Analytics?
Analytics is the process of transforming data from a complex or ambiguous form into something more readily understood. Insights gained can then be used for making better decisions.
In business, this might apply to improving the goals of your organization, or to a specific problem - optimizing processes, saving costs, or enhancing revenues.
Why is it Necessary?
If one or more of the following applies, analytics could probably help:
1. You face complex decisions
More dilemmas or decision factors than you can comfortably manage? Operational computer systems lack the intelligence to cope? Analytics can help you analyze complex situations, and build intelligence into key systems, to reveal the best options.
2. You're having problems with processes
Analytics can help, if one or more of your processes is broken, or needs to work a lot better. Or if many small decisions aren't being made well, and it's having a negative impact on your bottom line.
3. You're troubled by risk
Analytics can help quantify the risk of a new project or contract - which is key to controlling it. Analytics also assist in planning how best to balance that risk against the gains you expect.
4. Your organization isn't making the most of its data
Analytics focuses on data you collect about your organization or customers – extracting the most valuable information, so you can make decisions. It also highlights additional data you could collect, to increase the value even further.
5. You need to gain a competitive advantage
And, who doesn't? Analytics can help.
Insight, into Action
At the heart of business intelligence (BI) is the transformation of data into insight, and the translation of that insight into action. With the rise of Big Data - huge masses of structured and unstructured information - BI has become even more critical.
A Matter of Time
Due to its sheer volume, Big Data has increased the business opportunities available. But, it has also created challenges to capturing, storing, and accessing information. One of these is time. The usefulness of some data points declines rapidly, so it's imperative to accelerate the process of turning that data into information which can be acted upon.
Consider the data generated by mobile phones. This is routinely captured and stored by robust data warehouses, for thorough analysis. If the goal is to understand where a customer is right now, and to provide an offer based on his or her location, the capture and analysis of geospatial data must occur almost in real time - a serious technological challenge.
Projection, and Priority
Traditional business intelligence mainly uses data for projection: “steady state” customer information, purchasing history, inventory, etc. While this changes constantly in itself, for analytical purposes it can be used to project forwards - if you’re making decisions about what to do next month or next quarter, or even next week.
Priority data can be used to make decisions about what to do now, or in the next five minutes - if the correct information can be applied to the right question. In the mobile phone example, you might rely on a collection of GPS data (data for projection) from a population of cell phone users to make decisions about adding towers to a particular region.
But, to identify customers within a certain radius of a retail outlet and create an incentive for them to visit the store, you would need to act on their GPS information, now. The data has immediate priority.
Let's say a customer is in a mall where a retail clothing store has two branches. She uses an opt-in loyalty card to pay for her purchase, at Branch A. (The loyalty card already provides "projection" data from her purchasing history, and demographics).
Swiping her card at Branch A alerts the retailer that she's also near Branch B - and a text message offering a promotion there can be sent out.
The BI system has a lot to do in a very short time:
· Recognize a valued customer when she enters Branch A.
· Match her customer profile with a targeted promotion for Branch B.
· Deliver that promotion to her phone.
All within five or ten minutes; otherwise, she’ll likely be too far from store B to take advantage of the offer.
The most robust BI systems bring together an enterprise data warehouse, a discovery platform, and a data storage and refining platform.
How Analytics Can Work, for You:
1. First, recognize the opportunity
Begin with a general review of your organization, its departments, and processes. Look for difficult decisions that could benefit from the analysis of large amounts of data.
2. Make cultural adjustments
Build a culture that infuses analytics everywhere. Empower all your employees to make data-based decisions, instead of relying on instinct and past experience.
3. Be proactive about privacy and governance.
Ensure that the data being analyzed is safe, secure and accurate.
4. Use the right tools.
Invest in a Big Data and analytics platform that's tuned to the task of handling all types of data and analytics - regardless of form or function.
5. Use your insight.
Explore strategic options for business growth, using new perspectives gained from exploiting Big Data and analytics.