Determining the ROI of Data, Data, and MORE DATA!

Information is everywhere in the digital economy, and organizations want a good return on their investment in it. Data is so far-reaching that companies are starting to consider its actual monetary value. But the reality on the ground is that everyone is data rich and intelligence poor, making ROI elusive and difficult to obtain.

It's important to capture data to establish how well current methods are working and to decide what aspects of your program merit continued spending. Information is the basis for sound business decision-making and the means for staying current on new solutions that can help continued improvement. That's the upside. But data also has a direct impact on privacy issues and the complex considerations surrounding stewardship and business intelligence.

Data Is a Game Changer

Properly exploited, information improves and alters the way you make decisions, how you allocate your budget, how you engage with your customers, and how you conduct business in general. But getting the most out of the data available to you is an all-encompassing challenge that's impossible to ignore - and one which is often difficult to directly address.

How can data be used to achieve maximum benefit? How can the maximum ROI be realized from the use of this data? Options for addressing these issues are limited - whether internal or outsourced. It's a challenge, but one which has to be met.

Ignorance and Inertia Breed Indecision - Or Worse

Proper data handling sits at the heart of any digital transformation effort. A lack of understanding of what is making a business successful (or otherwise), coupled with a lack of innovation, can quickly negate any steps already taken in a digital journey.

Beyond that, an inability to properly manage business-critical information robs the enterprise of its ability to make informed decisions and perform on the same level as its competitors. The result is stagnation, loss of market share, a lack of credibility, and, ultimately, a backward slide into obscurity.

Fortunately, for those unwilling or unable to take on the responsibility of data handling themselves, there's a growing market of solutions and services to call upon. The trick is to know what to look for, and how best to proceed with what's on offer.

The AI Ecosystem

With its promise of real-time response and autonomous decision-making, artificial intelligence offers new ways of handling both operational and transactional business information. AI systems powered by machine learning (ML) or deep learning (DL) algorithms carry the potential for self-healing and self-governing processes that evolve in line with the quality and amount of data that they process and receive.

Used in line with several related technologies, such as robotic process automation (RPA), natural language processing (NLP), chatbots, and others, AI can disrupt and improve business functions in numerous ways - if it's deployed correctly and properly understood.

Self-service and information distribution channels using chatbots on websites, mobile apps, or as virtual "counselors" on various digital platforms become even more effective and realistic when combined with other technologies from the AI ecosystem.

Chatbots charged with AI can quickly learn and make sense of both data and context in real time. Machine learning algorithms enable chatbots to get smarter with each conversation they have with consumers. NLP injects chatbots with more personality and enables them to better understand the nuances of human speech. Chatbots deployed over several self-service channels can handle different portfolios, freeing human operators for more strategic work.

Chatbots can pull information from CRM and CMS platforms to extract data on the person they're interacting with. Integration with facial recognition, virtual reality (VR), and augmented reality (AR) - coupled with dynamic switching between voice and text and adjustments for each customer - may in future allow such systems to take on a personality based on the characteristics of the individual consumer.

In a similar manner, a combination of AI, NLP, and cognitive automation can empower RPA systems to handle unstructured data and dynamic rule sets, extending their capabilities and their ability to reduce human error and operational costs.

Data is the foundation stone for all of these technologies - whether it's pulled in from external sources, stored and analyzed, or disseminated.

Data Collection and Management

To deliver any kind of value or ROI in a business context, data must be readily accessible, accurate, current, clean, and complete. It also has to be readable and available for integration with other relevant systems or data - regardless of what format it's in. It shouldn't be surprising, then, that most data scientists spend 50 to 80 percent of their model development time on data preparation alone. Follow these guidelines for effective data collection and management:

  • Have a plan for your data: Begin by outlining exactly what your business goals are for the data you collect. Knowing this will enable you to keep only the information that's relevant to your goal, making your data stores smaller and easier to manage.
  • Focus on data quality: Regularly check your data for accuracy and relevance. Purging outdated or corrupt information will prevent it from negatively impacting your automations, analytics, and other business processes. You should also have procedures in place for handling duplicate or redundant data sets.
  • Simplify access to your data for those who need it: You'll need to draw a line here between security and convenience. To do this, you can set up specialized login and access permissions, and write network privileges into your security policy.
  • Prioritize security: Data protection and security best practices will safeguard your information against the threat of cyberattacks - whether from internal or external sources. This should include drawing up plans for rapid response in the event of a security breach or incident. With compliance regimes like GDPR and California 2020 now in place, it's more important than ever to follow the correct procedures in protecting the privacy of your organization, your suppliers, and your customers.
  • Use a data management solution with a data quality platform: Your data management software should have a data quality platform that incorporates data cleansing right into your data integration flow, with flexible manipulation techniques for data preparation. Of course, the solution should provide advanced analytics tools and reporting features.
  • Share metadata across your data management and analytics domains: A common pool of "data about data" empowers you to consistently repeat your data preparation processes throughout the enterprise. It also promotes collaboration and makes it easier to deploy the results of your analysis.
  • Have a backup plan: Data is no good to you if it's lost or unusable. Regularly scheduled backups, redundant storage, and a disaster recovery (DR) plan will protect your organization from such predicaments.

Data Analysis and Intelligence

Business intelligence (BI) and analytics yield greater ROI if users are comfortable with the tools available to them, which could be dashboards, enhanced reporting, or self-service analytics tools. The solution you deploy should satisfy the following criteria:

  • Freeing your data: Your BI platform should provide ready access to your information and allow that data to be extracted in a portable form, capable of integration with your core systems. Any predictive modeling, data mining, business intelligence, or machine learning tools should function without creating barriers to the underlying information.
  • Faster access, faster analytics: Critical decisions have to be made in as close to real time as possible. Fast access to information and powerful analytics tools enables this. BI tools should allow for easy integration with data sources and third-party tools.
  • Automation for better productivity: Automated data pipelines and storage systems are key drivers for analytical discovery and improved data management.
  • Delivering value: Your analytics toolkit should help you to quickly determine the value of a question and enable you to discard it if it serves no useful function. Dynamic and iterative BI tools can accomplish this.

Digital Advertising and Social Media - Tracking And ROI

Looking for likes, comments, brand recognition, and engagement on social media is only part of the story for ecommerce success. Social media is only one aspect of a wider strategy, and many organizations fail to acknowledge that their efforts on a platform should also yield ROI. For some, this may be quantifiable in terms of non-monetary values such as comments or newsletter subscriptions. For others, the value coming from social media investments may be measurable in terms of actual revenue.

Whatever the case, these considerations should be borne in mind:

  • Profit / investment x 100 = social media ROI %: This is a fundamental formula for expressing social media returns in monetary terms. For tracking the return from individual strands of your social media presence (e.g., whitepaper sales), you must delve deeper into the marketing funnel and monitor your audience's customer journey.
  • You'll probably need tools: Utilities and services like Google Analytics with UTM parameters or Facebook Ads with Facebook Pixel can help.
  • You should compare your performance to industry benchmarks: By making a comparative assessment of the market, you'll be able to determine how much your competitors are investing in their social media efforts, what level of return they're getting, and how your own revenue stacks up.
  • Be prepared to act on prevailing trends: Keeping a close eye on the market will empower you to act on evolving trends and change your strategies if necessary.

Predictive Analytics

Taken in the proper context, predictive analytics can improve your competitive edge by increasing efficiency in the workplace, reducing business risks, detecting fraud, and empowering you to better meet consumer expectations. These features should be a part of the predictive analytics solution you choose:

  • The software should reduce the time needed to collect data from multiple sources.
  • The platform should filter data according to the unique preferences or characteristics you specify.
  • The analytics engine should process information using various methodologies and algorithms.
  • Various tools should enable you to visualize data and results in different formats.
  • Reporting and presentation tools should provide a number of options for distributing analysis results to your stakeholders.
  • The interface should support customization and may provide APIs (application programming interfaces) for better integration with other business intelligence tools and your own systems.

Data Platforms

With Europe's General Data Protection Regulation (GDPR) now in force, and the California Consumer Privacy Act due to start making waves on Jan. 1, 2020, there's a need to tread carefully in the way that you acquire, store, share, or process customer data. A customer data platform (CDP) can assist in capturing privacy related consent from consumers while driving additional leads.

Customer data may be spread across a number of systems and databases, such as Customer Relationship Management (CRM) and email marketing platforms. And with marketing cloud services (organizations now use an average of 91 of them), it's easy to lose track of all that information.

A CDP integrates all of your customer data across the various platforms, tools, and databases. This gives a single, cohesive view of the customer. It can also provide an audit trail showing how customer data originated and where it came from. This empowers you to seek the necessary permissions from customers in Europe, California, or wherever consent-based interactions are mandated and enforced.

"Permission marketing" helps build trust among consumers. It also improves your handling of data - and, ultimately, the ROI that your information will generate.


The ROI of data is set to be a hot topic at Digital Transformation Connect 2019, taking place this September at the Rancho Bernardo Inn, San Diego, CA.

Download the Agenda today for more information and insights.



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