From Big Data to Big Ops: Process harmonization made easy

Customer behavior data are worth their weight in gold: Big Data is about collecting, analyzing and managing vast amounts of data from many different sources. On the other hand, Big Ops (short for Big Operations) focuses on the meaningful interconnection between the processes that are based on this data. The interaction between apps and automation with large amounts of complex data simplifies data management processes. Also, Digital Ops offers companies an opportunity to expand their customer knowledge and enhance the customer experience.

Key points

• The objective of Big Ops is to optimize the automation, communication and linking of large amounts of data between a company’s data management team and its users.
• By orchestrating and integrating your data with Big Ops effectively, you gain a significant competitive advantage.
• With coordinated processes and tools, you can give your customers a compelling customer experience.

Understanding Big Ops: The interaction between apps, automation and large amounts of complex data

In recent years, data management has continued to develop, and never before has it been possible for companies to collect such extensive amounts of data and analyze them. By collecting Big Data in so-called data lakes, companies can collect, analyze and manage diverse data in a raw data format in a central location. A frequent error: Often, companies do not use the data lakes created by them properly.

Big Ops is about linking the collected amounts of complex data and using them in the digital business at the same time. By orchestrating the amounts of data from different apps, automated and individual processes, human actions and state-of-the-art artificial intelligence tools, you get to know your customers better. You have the opportunity to acquire a significant competitive advantage through targeted customer communication and individualized customer journeys.

We can describe the value of the data on two dimensions:

1. the degree to which information, knowledge and insights have already been distilled from the data, and
2. the degree to which the collected data have already been activated and/or processed in the organization.

Big Ops is based on data connectivity and coordination, which are associated with data management and data governance (= control) – the availability and security of the data, based on the stringent data protection policies, is a challenge for many companies. Professional CRM and customer data management software will assist you with the upgrade from Big Data to Big Ops. They will turn your data lake into an interactive “data gold mine,” allowing you to offer your customers a perfect customer experience.

Using Big Ops: The effective use of data improves the customer experience

To offer their customers personalized content, companies favor the following data sources, according to a survey conducted by the Content Marketing Institute:

• Web analytics (72%)
• Social media (69%)
• CRM systems (58%)
• Surveys (58%)
• Customer service (57%)
• External databases (40%)

Source: https://financesonline.com/marketing-statistics/

To ensure that you succeed in optimizing the automation, communication and data, you need to link different Marketing Ops and synchronize all processes with each other.

Conclusion: Big Ops is leading the way in the transformation of marketing

Digitization also brought the transformation of marketing: Identifying touchpoints, omnichannel marketing, cross-selling, Big Data – all are indispensable for providing a personalized customer experience. Using Big Ops, you are ready to meet the challenges in marketing by optimizing the use of data, thus increasing your understanding of your customers.

We can summarize the most important findings on this topic as follows:

1. Organizations that successfully extract essential information from data and make them usable in the company as quickly as possible gain a competitive edge.
2. Appropriately, marketers, too, have to take a deep-dive into Big Ops to set up and manage meaningful marketing campaigns.
3. Data alliances and ecosystems are gaining importance, requiring more robust policies in data control and data governance.
4. In the future, we will have to address more profoundly topics such as ethics, prejudice and discrimination of and with data because of their growing importance.