[The following is from Megan Sullivan. I appreciate that she covers financial topics that I ordinarily wouldn't.]
Businesses that use big data for their financial operations tend to flourish. This happens for a wide number of reasons.
One reason, for instance, is that big data lets them know where they need to spend more to make more money. Another reason is that big data lets them know where to cut costs so that they're not losing money.
Protecting Big Data
Since big data is knowledge that can be used to make money, it needs to be safeguarded from hacker attacks. There is no point in developing clear business goals, understanding the business landscape, and learning how to effectively use the data, if the IT infrastructure is vulnerable to malicious attacks.
For big data to be secure, it’s important to get comprehensive monitoring of all network traffic. This means it’s necessary to use Internet software that ensures the entire network is visible to detect advanced threats or targeted attacks. The software used to gather intelligence about the network should be able to monitor all ports and cover a wide range of protocols. In other words, a financial firm that values its big data has to ask for the broadest protection available.
Benefits of Strategic Thinking
Financial service companies like banks and insurance use big data in strategic ways to increase their rate of innovation. Innovation can be defined as a blend of disruption and optimization.
Here, for instance, are four ways that financial service firms use innovation:
- They use innovation to increase profits and slash costs.
- They use innovation to create a positive cash flow.
- They use innovation to add more value to their products and services.
- They use innovation to find profitable trends hiding in plain sight that will increase market share.
So how does a financial service firm create successful analytical strategies with big data? How do they make productivity and profit gains?
Here are three ways that have proven valuable:
First, using big data to develop a clear business goal and monitor results.
Second, using big data to understand the business landscape and exploit new opportunities.
Third, using big data to chunk down information and serve it up in a timely way.
Let us take a closer look at these three strategies.
Clear Business Objectives
The financial business is complex, consisting of many moving parts. It's only too easy to become overwhelmed by many issues.
Does your business have a sufficiently robust IT infrastructure?
Is it staying on track with risk compliance?
Do marketing metrics justify costs?
While all these are legitimate concerns, they are management issues, not goals.
Business goals are more about creating a better future. They are about adding value that does not currently exist in either a company or a marketplace.
Here are three examples of some clear business objectives:
1. A measurable objective to improve customer experience to increase customer retention.
2. A quantitative objective to decrease fraud to reduce shrinkage and improve the bottom line.
3. A qualitative objective to offer more customized services to attract bigger clients.
Big data makes it easier to track the progress of these types of clear business objectives. These objectives are clear because they can be measured in either a qualitative or quantitative way. By contrast, murky objectives are those that rely on subjectivity, opinions, and perspectives.
Understanding the Business Landscape
It's almost impossible to drive innovation without an understanding of the business environment.
For instance, a life insurance company may find that other insurers are sloppy in the way they are underwriting risk. By noticing this gap in the business environment, a life insurance company can quickly begin to flourish by finding ways to underwrite risk in a much better way. The winning edge in business is not doing everything the same way as the competition, but finding out what the competition is not doing and filling in the gap in a significant way.
Effectively Using the Data
There are many ways to use the data more effectively.
Here are two examples:
Example #1: Use a data service platform to make all the relevant data accessible to those employees who need it as they work with clients. This data could commingle information from numerous, relevant sources.
Example #2: Use industry wide data to spot trends and unearth overlooked business opportunities.