Five Steps for Real-Time Decision Making
Written by GoodData Author |
During the last few years, data analysis has changed the speed of business. In a data-driven age of business, companies have to be able to make decisions in real-time in order to stay successful, and be one step ahead of their competitors.
Data flows through companies in stages of refinement: it's located and inputted, analyzed, then finally visualized and turned into easily consumable, useful insights communicated across the organization. But with the explosion of available information and the introduction of hoards of big data technologies that promise the ideals of becoming a true all data enterprise – companies need to make sure they're ready. Here are five things companies need to do in order to start.
1. Analyze accessibility and agility of their current analytic environment
Is the business intelligence platform easily accessible and easy to adopt? Does it require employees to log in to their own network? A lot of Salesforce.com's success is due to it's easy simple and easy access – people can use it anytime, from anywhere. The heart of a data-centered business should work the same way for both web and mobile consumers.
2. Don't be an aimless "data junkie" looking for their next fix
Successful analysts start with a purpose in mind, looking for a benefit that aligns to his or her specific role. For example, a marketing manager cares about the customer product, while a sales manager cares about the opportunity. Likewise, the corresponding data set this person accesses should be built with this context in mind. Once you ensure that you have access to the right data, then you can make the data work for you, not against you – and the way to do that is to first define a specific business problem.
3. Contextualize your metrics
So you've found your data. Now what? The first step is to contextualize the metrics that you are evaluating. For example, if the data shows that sales are improving week over week, what is the insight that a team member shows to his or her manager? Is this insight due to A) new employees who have raised the productivity level of the team; B) a great marketing campaign that increased awareness by 22 percent; C) a seasonal trend? Being able to draw out the details behind this key performance indicator – or KPI – is what will illustrate is significance.
4. Communicate your findings
Now that the data has provided the answer, it's up to the employee to follow through with socializing these insights so that they become part of the operating fabric of the organization. When everyone in the organization from the admin to the salesforce to the CEO has visibility into the analytic processes, the data becomes infinitely more valuable.
5. Optimize and evolve this KPI
How do you make this better? This is the question to ask of the KPI once you become familiar with it. Do not let it get stale, as this prevents subsequent insights from forming. Analytics is a process, and as such, all the data, and all it's aggregate distillations need to change and evolve. As people consume information, they become smarter and when they become smarter, they change their questions.
This leads us back to point number 1, regarding agility. Can your analytic environment evolve at the pace that you require of your business? What if you add information or new sources? Can you move forward as fast as you think of the next idea.
Written by GoodData Author |
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