We’re Living in a Metrics-Driven World
Analytics-Driven Organizations know how to turn data sources and solutions into insights that offer a competitive advantage
“The goal is to turn data into information, and information into insight” — Carly Fiorina
We live in a data-driven world. For Fortune 500 companies, the value of data is clear and compelling. They invest millions of dollars annually in information systems that improve their performance and outcomes. Independent businesses have the same need to be data-driven; however there’s a persistent entrepreneurial resistance to becoming truly metrics-driven.
Founders are often tempted to postpone building necessary metrics in favor of spending time and resources on building products. While that might work in the short-term, it will very quickly come back to haunt them.
Very few companies have successfully achieved exponential growth, raised capital, or negotiated strong exits without first having a solid analytics model that has been iterated upon for many months or years.
The Analytics-Driven Organization
The importance of big data in the business world can’t be overstated. We know that there’s a enormous amount of valuable data in the world, but few companies are using it to maximum effect. Analytics drive business by showing how your customers think, what they want, and how the market views your brand.
In the age of Digital Transformation, almost everything can be measured. In the coming year this will be a cornerstone of how businesses operate. Every important decision can and should be supported by the application of data and analytics.
To keep competitive today, you need to “think ahead” and answer questions in real-time to provide alerts to mitigate negative impacts on your business, and you need to predictive analytics to forecast what’s going to happen before it ever does so you are prepared at any given point in time.
In 2017, data will become more intelligent, more usable, and more relevant than ever. Cloud technologies, primarily Amazon Web Services (AWS), has opened the doors to affordable, smart data solutions that make it possible for non-technical users to explore (through visualization tools) the power of predictive analytics.
There’s also an increasing democratization of artificial intelligence (AI), which is driving more sophisticated consumer insights and decision-making. Forward-thinking organizations will approach predictive analytics with the future and extensibility in mind.
Analyzing extensive data sets require noteworthy compute capacity that can fluctuate in size based on the data inputs and type of analytics. This characteristic of scaling workloads is perfectly suited to AWS and their Marketplace pay-as-you-go cloud model — where applications can scale up and down (and in and out) based on demand.
In 2017, entrepreneurs will learn how to embrace the power of cloud analytics.
The ubiquity of cloud is nothing new for anybody who stays up-to-date with Business Intelligence trends. The cloud will continue its reign as more and more companies move towards it as a result of the proliferation of cloud-based tools available on the market. Most of the elements — data sources, data models, processing applications, computing power, analytic models and data storage — are located in the cloud.
No matter the role, no matter the sector, data is transforming it. Some companies have restructured themselves, their internal processes, their data systems & their cultures to embrace the opportunities provided by data.
At their core, the best data-driven companies operationalize data. Data informs the actions of each employee every morning & evening. These businesses use the morning’s purchasing data to inform which merchandise sits on the shelves in the afternoon, for example.
The Analytics-Driven Organization has also developed functional data supply chains that send insight to the people who need it. This supply chain comprises all the people, software, & processes related to data as it’s generated, stored, and accessed.
These businesses also have created a data dictionary — a common language of metrics used by the company to create a universal language used throughout all departments of the company.
As companies become analytics-driven, they aren’t just enjoying incremental improvements. The benefits enabled by analytical data processing becomes the heart of the business — enabling new applications and business processes, using a variety of data sources and analytical solutions — giving insight into their data never dreamed of and giving them a great competitive advantage.
The Types of Analytics and Their Use Cases
Descriptive Analytics
Uses business intelligence and data mining to ask “What has happened?”
Descriptive Analytics mines data to provide trending information on past or current events that can give businesses the context they need for future actions. Descriptive Analytics are characterized by the use of KPIs. It drills down into data to uncover details such as the frequency of events, the cost of operations and the root cause of failures.
Most traditional business intelligence reporting falls into this realm, but complex and sophisticated analytic techniques also fall into this realm when their purpose is to describe or characterize past events and states.
Summary statistics, clustering techniques, and association rules used in market basket analysis are all examples of Descriptive Analytics.
Diagnostic Analytics
Examines data or content to answer the question “Why did it happen?”
Diagnostic Analytics is characterized by techniques such as drill-down, data discovery, data mining and correlations. You can think of it as the casual inference and the comparative effect of different variables on a particular outcome. While Descriptive Analytics might be concerned with describing how large or significant a particular outcome is, it’s more focused on determining what factors and events contributed to the outcomes.
As more and more cases are included in a particular analysis and more factors or dimensions are included, it may be impossible to determine precise, limited statements regarding sequences and outcomes. Contradictory cases, data sparseness, missing factors (“unknown unknowns”), and data sampling and preparation techniques all contribute to uncertainty and the need to qualify conclusions in Diagnostic Analytics as occurring in a “probability space”.
Training algorithms for classification and regression techniques can be seen as falling into this space since they combine the analysis of past events and states with probability distributions. Other examples of Diagnostic Analytics include attribute importance, principle component analysis, sensitivity analysis and conjoint analysis.
Discovery Analytics
Approaches the data in an iterative process of “explore, discover, verify and operationalize.” It doesn’t begin with a pre-definition but rather with a goal.
This method uncovers new insights and then builds and operationalizes new analytic models that provide value back to the business. The key to delivering the most value through Discovery Analytics is to enable as many users as possible across the organization to participate in it to harness the collective intelligence.
Discovery Analytics searches for patterns or specific items in a data set. It uses applications such as geographical maps, pivot tables and heat maps to make the process of finding patterns or specific items rapid and intuitive.
Examples of Discovery Analytics include using advanced analytical geospatial mapping to find location intelligence or frequency analysis to find concentrations of insurance claims to detect fraud.
Predictive Analytics
Asks “What could happen?”
Predictive Analytics is used to make predictions about unknown future events. It uses many techniques from data mining, machine learning and artificial intelligence. This type of analytics is all about understanding predictions based on quantitative analysis on data sets.
It’s in the realm of “predictive modeling” and statistical evaluation of those models. It helps businesses anticipate likely scenarios so they can plan ahead, rather than reacting to what already happened.
Examples of Predictive Analytics includes classification models, regression models, Monte Carlo analysis, random forest models and Bayesian analysis.
Prescriptive Analytics
Uses optimization and simulation to ask “What should we do?”
Prescriptive Analytics explores a set of possible actions and suggests actions based on Descriptive and Predictive Analyses of complex data. It’s all about automating future actions or decisions which are defined programmatically through an analytical process. The emphasis is on defined future responses or actions and rules that specify what actions to take.
While simple threshold based “if then” statements are included in Prescriptive Analytics, highly sophisticated algorithms such as neural nets are also typically in the realm of Prescriptive Analytics because they’re focused on making a specific prediction.
Examples include recommendation engines, next best offer analysis, cueing analysis with automated assignment systems and most operations research optimization analyses.
Sentiment Analysis
The process of determining whether a piece of writing is positive, negative, or neutral.
Sentiment Analysis is also known as opinion mining — deriving the opinion or attitude of a speaker. Social media tweets, comments, & posts typically feed sentiment analysis. This is a sub-category of general Text Analytics. A common use case of sentiment analysis is to discover how people feel about a particular topic.
Geospatial Analytics
There is a growing realization that by adding geographic location to business data and mapping it, organizations can dramatically enhance their insights into tabular data.
Geospatial Analytics, or Location Analytics, provide a whole new context that is simply not possible with tables and charts. This context can almost immediately help users discover new understandings and more effectively communicate and collaborate using maps as a common language.
When you can visualize millions of points on a map, use cases include route planning, geographic customer targeting, disease spread and more.
The Culture of Digital Transformation
Change is going to happen whether you pursue it or not — you only need to look at how the role of cloud computing in 2016 has evolved to understand. Modern enterprises succeed when they adapt to industry and marketplace shifts and incorporate new technology into company culture and regular operations.
Digital transformation isn’t only about technology, it’s about bringing together the power of technology with a culture that embraces the change that it can lead for the organization.
Proactive innovation is one of the best ways to stay competitive in an evolving marketplace. New technology needs to be assessed, tested, analyzed, and judged more quickly than ever. Businesses can no longer afford to waste time and resources implementing new tools that offer no real value. This means a “Fail fast, to succeed faster,” mentality.
Some projects will work straight away, others will have significant learning curves. The faster your organization can go from idea to implementation the more it can embrace opportunities to transform and even disrupt markets and internal business models. We’ve already talked about adaptability, but that plays a major role here as well.
If a company has an adaptive culture where new tech can be easily integrated — or is at least encouraged — that enterprise is set up for long-term success.
Kim Schmidt is President & CEO at DataLeader.io. You can follow Kim on Twitter @dataleader
