Analytics that Make a Difference

Analytics that Make a Difference

Today’s digital economy is fueled by data. Many organizations are mandating that everything must be data-driven and decisions are no longer left to gut instinct. All company decision and actions should be based in facts which are fueled by algorithms that predict optimal outcomes. In his report, Big Data in Big Companies, Thomas Davenport from the International Institute for Analytics, interviewed 50 organizations to understand their data usage and found the following ways that organizations gained value through tracking analytics.

Cost Reduction: Technologies such as Hadoop and cloud based analytics such as SAP Cloud Analytics provide significant savings to organizations for their data storage needs. Another cost reduction measure is using analytics and data derived from the customer experience to interact with customers in real time to provide targeted offers and services.

Time Reduction: Organizations have the ability to reduce the cycle time for complex and large-scale analytical calculations from hours or even days to minutes or seconds.

Faster Organizational Decision Making: Generating and analyzing new sources of data to a granular level quicker allows organizations to make decisions more quickly and more accurately.

New products and services: The ability to gauge customer’s current and future needs as well as their satisfaction of products/services through analytics, allows organizations to develop relevant new products/services saving them valuable development time.

Types of Analytics

Analyze for important data

Organizations pursue data and analytics activities for a number of reasons which may include buidling a competitive advantage or improving their customers’ experience. The question is, “What data is needed to make the decisions that provide the outcome organizations want?” That’s where Analytics come in.

First, let’s look at the types of analytics that are available.

Descriptive analytics: answers the question of what happened?

Descriptive analytics sorts through raw data from multiple data sources to give valuable insights into the organization data history. The drawback is that the findings only indicate if something went right or wrong and not why it happened. Typically, highly data-driven companies combine descriptive analytics with other types of data analytics to get a full picture.

Diagnostic analytics: answers the question of why did it happen?

Diagnostic analytics allow organizations to dig deeper, find anomalies and identify patterns in the data, giving them deeper insight into a particular problem. Diagnostic analytics fall into three categories:

  • Identify anomalies: results from descriptive analysis help to identify areas that require further study since they raise questions such as, why sales have increased in a particular region even though no change was indicated in marketing or why there was a sudden drop in website traffic without an obvious cause. These questions cannot be answered just by looking at the data.
  • Drill into the analytics (discovery): At this point organizations must identify the data sources that will help them explain these anomalies which will require looking for patterns outside the existing data sets and possibly pulling data from external sources.
  • Determine causal relationships: Looking at events that may have resulted in the anomaly may provide insight. Probability theory, regression analysis, filtering, and time-series data analytics can all be useful for uncovering hidden stories in the data.

Predictive analytics: answers the question of what will happen?

Predictive analytics uses the findings of both descriptive and diagnostic analytics to detect patterns and exceptions to help predict future trends, which is a valuable tool for organizational forecasting. Although there are a number of advantages predictive analytics brings, it is still just an estimate and depends highly on the accuracy, quality and stability of the data. Therefore, it requires caution and continuous optimization.

Prescriptive analytics: answers the question of what should I do?

Predictive analytics are used to eliminate a future problem or take full advantage of a promising trend. This process requires not only historical data but external information as well. Prescriptive analytics uses complex tools and technologies such as machine learning, business rules and algorithms making it sophisticated to implement and manage. Before deciding to adopt this type of analytics, an organization needs to compare required efforts vs an expected added value.

Source: Gartner (October 2016)

Data Analytics trends for 2019 and beyond

Are you getting the most value out of your data? Provided in this link is a video panel webinar with Gartner experts discussing key data & analytics topics, including strategy, tools, best practices, organizational design and service providers. In the webinar they answer the following questions.

  • How effective are your current data and analytics initiatives
  • What trends will most impact how you utilize data and analytics in 2019 and beyond
  • What must you do to maximize data and analytics in your organization

Next generation analytics

At the Gartner Data & Analytics Summit in Sydney this year, augmented analytics was deemed the future of data and analytics. Augmented analytics uses machine learning to automate data prep and natural language processing (NLP) which is the ability of a computer to udnerstand, interpret and manipulate human language as it is spoken. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.

Why are Analytics Important

In today’s digital world, organizations are collecting data throughout the customer journey either through mobile app usage, digital clicks, social media activity and more which provides a fingerprint of the customer. Organizations have the opportunity to analyze this data to drive positive outcomes for themselves and their customers. Examples of the ways the data can be used by organizations is provide below:

  1. Be Proactive & Anticipate Customer Needs

Competitiveness has pushed organizations to not only acquire customers but understand and anticipate their needs so that they can optimize the customer experience and develop long lasting customer relationships. In the same tone, customers expect organizations to know them and to provide a seamless customer experience with each transaction.  As a result, organizations take multiple customer identifiers such as cell phone, email and address and convert these to one single customer ID so that they can deliver contextually relevant, real-time experiences.

  1. Delivering Relevant Products/Services

Products/services are the largest investment organization can make and they need to recognize the trends that drive innovation, new features and services that will appeal to their current and future customers.  By combining data collation from 3rd party sources such as Facebook, Twitter, Yelp and others, with analytics will help organizations stay competitive. This data will provide organizations the information needed when their customers preferences change, new technology is developed as well as prepare them for market shifts and provide new products/services before it is requested.

  1. Personalization & Improve Customer Experience

Customers are engaging more and more via digital technology and organizations must be able to react in real time and make the customer feel personally valued. Analytics allows organizational interactions with their customers to be based on the personality of the customer. By understanding customers’ attitudes and considering factors such as real-time location can help organizations to deliver personalization in a multi-channel service environment.

Organizations want to reduce the risk of damaging the customer experience and losing brand loyalty. By reviewing the data analytics, organizations can control the process and optimize business operations to efficiently and effectively fulfil customer expectations and deliver a personalized customer experience.

  1. Mitigate Risk & Data Security

Protecting the personal data of consumers is extremely important. Not only are too many consumers and companies suffering from data theft every year, but billions of dollars are spent to recover from data breaches.  Efficient data and analytics capabilities will deliver optimum levels of fraud prevention and provide organizations with the ability to not only quickly detect potentially fraudulent activity but anticipate future activity as well. It may also be able to identify and track perpetrators. Please refer to our recent blogs on 5 Reasons Why the California Consumer Privacy Act Should Matter to You or The Definitive Guide to your GDPR Checklist to learn more about how customer data security regulations can affect your organization.

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