Revenge of the Nerds! Why Data is Having a Renaissance

Big-Data 

Big data has been around for a while, but it’s currently undergoing a renaissance that has the nerd population celebrating.

Remember when cars were first coming out?

Of course you don't, unless you're a vampire reading this tech advice. But still, the point remains; when the automobile was first created, they were clunky, large and ineffective. Just look at this Benz Patent-Motorwagen built in Germany in 1885.

Benz motorwagen

It wasn't until decades later when Henry Ford revolutionized the industrial world with the assembly line that the potential of the motor vehicle was actualized. Advanced cars like the Model T were being produced on such a massive scale that the world was transformed in the process.

Big data, a few decades ago was clunky, cluttered and generally unusable. But now, it's at the same type of defining moment as October 7, 1913: Just as we can no longer manage without vehicles, we’re at the stage where businesses can’t be competitive without mastering the use of data and the business intelligence (BI) it delivers. The science of BI has become ubiquitous. It is the cornerstone of successful business technologies, and it is revolutionizing the way that businesses operate. If you're not tapping into the gold mine of your business data, it's time to join the new frontier before it's too late.

How Data is Being Reborn

This rebirth of the way we use data is driven by various factors. Artificial intelligence is changing how we live, in everything from how we assess wars to the way companies deliver milk. Let’s take commuting as an example. According to a report by the Texas Transportation Institute, commuter times rose steadily year-over-year prior to 2015, to reach 42 hours of rush-hour traffic delays per commuter. Until recently, this was more than a full work week per person per year, resulting in $160 billion in productivity losses.

By using anonymous location data from smartphones, Google Maps (Maps) can determine the speed of traffic movement at any point in time. It can include in the software incidents like construction and accidents reported by users. This alerts users before they reach a delay, enabling Maps to reduce commutes by suggesting the fastest routes to and from destinations. Machine learning enables Maps to discover features that matter to users, and sophisticated reporting mechanisms provide this in the form of business intelligence.

What Data-Driven Means

With all this valuable information available, companies that aren’t data-driven in every aspect of their business from operations to finance and marketing are missing out big-time. Being data-driven requires you to:

  • Support your human decision making with data and analytics.

  • Run business experiments and split tests to keep abreast of what’s working and what isn’t, so you can tweak it.

  • Make large acquisitions primarily for data, such as Microsoft's $26b acquisition of LinkedIn.

  • Combine data from different sources together to obtain a better understanding of your customer.

  • Analyze new data sources even though potential value is uncertain. You’ll only reap the benefits once you implement the insights you learn.

Turning Data into Decisions

Using your data to make decisions involves turning it into actionable business intelligence. In a nutshell, BI is a set of combined technologies, methods and processes that translate raw data sets into useful information. This output enables more effective decision-making and planning to take place in the organization.

Turning your data into BI requires merging and analyzing data from all sources, identifying patterns, measuring deviations, and considering all in the context of your business. When you know, for example, how many people with a certain profile have researched or purchased your product in a specific period, you can formulate a reliable theory that it's popular with that segment of your audience.

The business intelligence stages are:

  • Data sourcing, which includes gathering information from multiple records across your organization and industry;

  • Analysis, which takes information and parses it into a solution programmed to give you insights you need.

  • Situation awareness, including observation and understanding of what’s going on around you and forces that may be at work.

  • Risk analysis and decision support, which include taking what you know and using it to evaluate current and future risks versus rewards. When you have this information, identify and make informed decisions based on the findings.

Big Data Trends that Impact Business

By the end of 2018, Forrester predicts 70% of enterprises will have implemented AI. This is up from 40% in 2016 and 51% in 2017, making it crystal clear big data and AI are here to stay. Some of the ways this impacts business include:

Understanding, targeting and serving customers: BI enables companies to get a more complete picture of their customers, their preferences and behaviors. This is essential for predictive modeling. It’s also important for customer service operations, a focus which is critical for all e-commerce businesses. Statistics on unhappy customers and poor service are remarkable, with 91% unwilling to use a provider if they have had a poor experience. BI provides insights you need to track customer experiences and add predictive monitoring to identify and resolve problems before customers are aware of them.

Changing how marketing is done: Personalized targeting by marketing teams is growing in leaps and bounds, as a result of BI. Conclusions drawn from the marketing stack enables AI to identify when prospects on any platform start searching for a product. Businesses can then automatically respond, providing the prospect with a better user experience than before.

Improving efficiencies: Rich data tells a story, and smart companies are listening. Business intelligence analysis makes it easier to recognize constraints, and predictive models enable executives to determine whether these are binding or not and to remove or resolve them where possible. Getting rid of constraints delivers improved efficiencies and cost-savings, both of which contribute to your bottom line.

Importance of Quality Data for Predictive Revenue Models

Whatever type of business you operate, predictive analytics is an innovative way to capitalize on data you hold and use it to determine a predictable, profitable pipeline of new business. A Forbes Insights survey showed 89% of 306 B2B executives reported increased revenue as a result of predictive marketing practices. Using this model requires quality data, however, or your results will be less than stellar.

A predictive revenue model creates consistent, year-over-year formulae for business growth based on a combination of historical data and business intelligence. It predicts or forecasts the value of future revenue, which extrapolates to include an understanding of how your funnel works, the average size of your deals and the time frames in which these occur. Benefits of using this model include:

  • Improved production efficiencies, based on better forecasting of inventory requirements and production rates, and the use of historical data to anticipate possible failures and prevent the errors that caused them.

  • Stronger competitive advantages, because of insights gained into information that already exists but needs to be “dug up.” Find out why customers chose you over competitors and determine the unique selling points you can promote to maximize this.

  • Reduced risk, through the use of financial analytics to construct customer profiles based on data that can inform product development and marketing decisions.

  • Better fraud detection, based on pattern recognition that enables AI to spot anomalies that could indicate a threat, highlighting and preventing them from proceeding.

  • Enhanced marketing capabilities, which makes the most of consumer data to predict where you should be focusing your efforts, what’s working and what isn’t, and how you can cross-sell and upsell.

  • Meeting consumer expectations, by having a clear visual representation of your customers and what they want. This enables you to provide what they want, in a manner tailored specifically at getting them to respond.

When you’re basing your business intelligence on data you have available, you’d better make sure it’s good quality information to start with. As the old saying goes: “Garbage in, garbage out,” and nobody business wants to spend money on processes based on garbage.

Popular BI Methods and Software

So, you’re aware of all the reasons for using business intelligence in your company, and you understand the benefits available to you from doing so. You know the data you have drives the entire process, but with your data hidden in multiple different systems and formats, how exactly do you gather it all together, combine it into a usable format and determine what the most useful patterns are?

Fortunately, that one’s easy to answer: software. There is a wide range of applications available that you can use to do the job for you, and they come in different shapes and sizes (and prices!) to ensure there’s a program right for every company. Here are some of our favorites:

Sisense

Sisense has an easy drag-and-drop process that you can use to combine all your data and display it in a dashboard. It has top-notch functionalities, a free trial plan, and advanced In-Chip technology. The program is user-friendly and can drill down into large, complex datasets for instant answers, and integrates your other business systems to create a centralized data hub.

Tableau

Tableau is an easy-to-use, self-service system that provides fast answers to complex queries. It has a dashboard for commenting, security permissions at any level, deploys on either the cloud or your local server.

databox

Databox is a “mobile-first” BI tool aimed at sales and marketing crews. It works on devices including smartphones, tablets, and digital watches, using native apps for iOS and Android. It gathers data from a ton of third-party sources, including HubSpot.

Salesforce, Stripe, and Google Analytics offer a collection of pre-made templates and data filtering using drag-and-drop. It has collaborative capabilities with scorecards and regular summary reports and works well for teams in multiple locations.

The current data renaissance is almost a revolution, with top-rated business intelligence software available for almost every size and type of company. There’s no reason for any business to forego using BI to provide insights that guide your predictive analytics model, so what are you waiting for? Start tapping that gold mine.

Need an employee to implement all this data genius for your business? Or maybe you need a whole team of data pros for the price of one to make this a reality. Look no further:

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