Big data has emerged as the most eagerly sought solution by businesses to succeed in today’s challenging marketplace. The same applies to the lending sector that is consistently showing signs of recovering from the economic slowdown. The next step towards bigger margins and sustained growth perhaps lies in uniform adoption of Big Data among these businesses. Some questions remain unanswered, particularly among those who aren’t familiar with the benefits of using big data.

Still Apprehensive about Big Data?

Lending institutions are always on the lookout for immediately effective tools that can help them optimize their processes towards more productivity. This applies to internal workflows and when dealing with customers across marketing channels. The ability to handle big data seems like the only comprehensive answer at the moment. Big data helps to identify problems among legacy workflows and recently adopted processes. Lending institutions should understand that they might not have the IT and manpower capability to use big data for maximizing their gains. This isn’t a predicament as such.

Companies have the freedom to choose vendors who specialize in helping businesses adopt, use, and manage big data. A capable vendor should be able to offer innovative methods that can be customized according to the requirements of a business. This includes progressive data analysis that can handle current and remotely located legacy data. The idea is to choose technologies that aren’t overtly expensive. Such tech solutions shouldn’t present the problem of recruiting more manpower. Their emphasis should be on making the current/employed workforce smarter and the business more sensitive to customer requirements.

How big is Big Data?

This is a common misconception—Big data doesn’t always refer to enormous data volumes. The bigness is in fact quite relative. The problem isn’t so much with too much data but with lack of solutions to collate all data and analyze it for deriving actionable information. This is a more practical definition of big data. When data doesn’t make sense, it stops being a part of big data. Labels will come and go and nomenclatures will continue to evolve as the industry becomes more data driven. However, the core meaning of big data will remain the same—the quality of data and its utility is the primary consideration.

Ready for Big Data?

Before beginning their journey towards adopting big data, lending institutions should create a blueprint. A basic plan that can identify current data problems and the most probable challenges is good enough. For instance, retrieving legacy data that might contain critical information is a common challenge. Some goals should be established with respect to leveraging information. Data analytics personnel should be addressed, insisting that the management requires simplified and actionable data and clearly-defined ways to measure success.

Businesses processes/activities where big data is most relevant include:

  • Scrutinizing remote organizational assets
  • Making sales process more robust
  • Deeper risk evaluation
  • Promotional or marketing campaigns
  • Data security

Overwhelmed by Data Plumbing Challenges? You are not Alone!

This is a commonly discussed topic among data specialists—the difficulty in plumbing for data among businesses that don’t have a systematic data structure. Conversations about using more graph database and environments similar to Hadoop have become rather common. Retrieving information stored in regional databases isn’t easy. There is also a chance of coming across business data where big data isn’t too relevant, such as:

  • Inventory management
  • Ledger processes
  • Payroll processes
  • Vendor management
  • Conflict resolution

Businesses need to understand that the issue of not having a pre-existing data infrastructure is not exclusive to them. It is the skills of the big data solutions provider that will decide how unstructured data can be made useful. Sometimes, not aligning unstructured data seems like the wiser thing to do. Some unstructured data might not be worthy of a long process of reorganizing it. Sometimes, just analyzing these data types is a better decision.

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