The Risks and Rewards of Emerging Data Technologies:  Part 2 

In Part 1 ,  we discussed the data management implications for 2 emerging technologies;   cloud computing and in memory databases.    In this section we will cover 2 more: 

Crowdsourcing Technologies

Crowdsourcing leverages a large network of people to solve problems or solicit ideas. While it’s not a solution to every problem, crowdsourcing provides businesses with an alternative to data enrichment that is worth considering for certain data management challenges.

Crowdsourcing companies collect and validate information from various Internet sources, including social media. Traditionally, companies hire their own back-office data operations employees to enrich their customer account and contact information.

Crowdsourcing is a cost-effective alternative to this method. The “crowd” is dispersed all over the world making access to local information for address verification and other regional company verification easier. Companies leading the way in facilitating crowdsourcing have come a long way. The platforms they have established ,now ,are not only efficient in channeling the tasks to the crowd but also have  many capabilities that ensures high quality of the data / information received .  For example, you can have multiple inputs to the same question / task to ensure  you receive the information with a  higher confidence level.   Also, the cost per transaction using the crowd is usually below the traditional resourcing model.

The crowdsourcing approach can also be used internally in your company.  Gauging the overall quality of data in a database is a challenge, as the sheer volume of information is often overwhelming. The crowdsourcing approach can improve relevancy, accuracy and usefulness of data by making it easier for employees to provide feedback at a micro level, and for businesses to then bundle that feedback and analyze at a high level. Data management professionals should strive to give their colleagues an end-user experience that’s aligned with their own personal digital behavior. For example, a simple “like” approach (think: Facebook) is an easy way to engage employees and involve them in testing the usefulness of data in core applications.

Mobile Technologies

As smartphones and tablets become ubiquitous, mobile applications are growing in staggering numbers, being developed for every aspect of the workday, from HR transactions to CRM to marketing workflows. Forward-thinking companies must realize the true business value of mobile applications.

How can a mobile app help your business clean up its data? Many companies struggle to keep account information current across various teams, for example. Traditional CRM applications are often not designed with data management best practices in mind. By making the app more user friendly — easy to update customer data and add “like” functionality to help with relevancy — businesses will empower members of any team to be more proactive about data quality.

To design mobile applications that lead to more effective data management, consider the following:

  1. 1. Check for data quality standards at creation time, including duplicates and address standardization. If designed appropriately, your mobile apps shouldn’t take a performance hit.
  2. 2. Limit the free-form entry of critical fields; use pull-downs and auto-populate fields.
  3. 3. Significantly reduce the number of screens and clicks required to create or update a record.
  4. 4. Default to the most-used views and records.
  5. 5. Plan for the ongoing need to archive old records.

Mobile applications can go a long way toward simplifying data entry and correction, but only if designed correctly. Data management professionals should be sure to be involved in app development and mobile strategy from the outset.

In addition to designing a new mobile application,  mobile applications exist that can scan a business card and  then create  a contact . The application can take a picture of the business card which it then translates to the right fields using OCR technology.  The digital contact can later be channeled to the company’s data operation team to review and make final corrections before it is loaded into the CRM system. 

Bringing it all Together

Big data isn’t the end-all, be-all of your company’s data management. Data professionals must continually remain involved in the evaluation, design and deployment of emerging data technologies like the cloud, in-memory databases, crowdsourcing, and mobile apps.

A major challenge for many businesses remains attempting translate data accumulation into meaningful data-driven action. When considering a new solution, data leaders should first ensure that a deployment would do no harm to the existing data quality programs. With that hurdle cleared, it’s then time to determine how to exploit the technology to improve data quality and data management, and to explain to your colleagues why this improvement could be such a game-changer.

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