Hay

As men­tioned in a pre­vi­ous blog post Turn­ing Big Data into Big Oppor­tu­nity for Finan­cial Ser­vices, Big Data typ­i­cally refers to the stor­age and man­age­ment of large­quan­ti­ties of data. As a result, the focus is placed more on the col­lec­tion of every­thing instead of iden­ti­fy­ing what mat­ters. If you are look­ing for a nee­dle in a haystack, why add more hay?

Data col­lec­tion and analy­sis is impor­tant, but if you are not able to act on it, what is the ben­e­fit from col­lect­ing more data? Through mar­ket­ing that har­nesses Big Data (Mar­ket­ing Big Data), orga­ni­za­tions can iden­tify the data that mat­ters and put it into the hands of those who can act on it. Big Data tools for mar­ket­ing are not designed to replace large data envi­ron­ments and solu­tions, but to sup­ple­ment them. Through Big Data tools, mar­keters can cull insights from vast and dis­parate data sources and iden­tify sig­nals to trans­form cus­tomer engage­ments, offer­ing more opti­mized expe­ri­ences that drive invest­ment returns.

Obtain­ing Value From Big Data

In order to real­ize the ben­e­fits of Mar­ket­ing Big Data, it is impor­tant to have a process and tools in place that will take your orga­ni­za­tion from the col­lec­tion of var­i­ous data points to the exe­cu­tion of mar­ket­ing expe­ri­ences. Con­sider the fol­low­ing framework:

  1. Ingest – Begin with man­ag­ing the data. Iden­tify the tools and processes needed to col­lect, con­nect, and store data points from the var­i­ous sources.
  2. Dis­till – Refine the data through analy­sis. Auto­mated algo­rithm may also help expe­dite the find­ing of valu­able insights.
  3. Curate – Assem­ble the insight found while dis­till­ing the data into pack­ages that can eas­ily be con­sumed. This may be through reports for peo­ple, or through tools for other sys­tems to use.
  4. Syn­di­cate – Deliver and pub­lish the insights to the peo­ple and sys­tems that need to be aware of them.
  5. Opti­mize – Use what you have learned to test and improve. Learn from past work and get a lit­tle bet­ter each time.

Big Data Framework

Fol­low­ing a frame­work will allow you to not only act on Big Data, but also iden­tify gaps in processes or tech­nol­ogy in order to max­i­mize the ben­e­fit of Mar­ket­ing Big Data.

What About Privacy?

To date, many com­pa­nies in the finan­cial indus­try have remained cau­tious about delv­ing into big data due to the industry’s strict pri­vacy and com­pli­ance reg­u­la­tions. For the com­pa­nies in this indus­try, pri­vacy is para­mount. If even a small amount of per­sonal infor­ma­tion escaped a “data vault,” it could be detri­men­tal to a cus­tomer and the institution’s over­all brand, includ­ing a loss in rev­enue from decreased busi­ness or from reg­u­la­tory fines.

Since pri­vacy is crit­i­cal, gov­ern­ment agen­cies heav­ily reg­u­late the use of per­sonal infor­ma­tion for finan­cial ser­vices and insur­ance com­pa­nies. In addi­tion, many insti­tu­tions have their own poli­cies in place to fur­ther pro­tect and dic­tate the use of data; those poli­cies can be even stricter than estab­lished gov­ern­ment reg­u­la­tions. Addi­tion­ally, insti­tu­tions in this indus­try need to put forth the effort to con­vince reg­u­la­tory groups that data is used appro­pri­ately and to reas­sure cus­tomers that their per­sonal infor­ma­tion is secure.

How­ever, per­sonal or sen­si­tive infor­ma­tion does not need to be made pub­lic in order to use for mar­ket­ing. Most data sources and cus­tomer infor­ma­tion lie in secure loca­tions, where mod­el­ing and analy­sis may occur. Anony­mous cus­tomer seg­ments may be cre­ated based on rules and attrib­utes from analy­sis of known infor­ma­tion behind secure walls. Use what is known to mar­ket to the unknown.

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