Most modern companies have more data than they know what to do with, but that doesn’t mean your department has to boil the ocean to turn data into valuable business insights. Departmental leaders can set the tone at the top, ruthlessly prioritizing what’s important to move the business forward. You can rein in analysis paralysis, even while unleashing the power of data analytics through Analytic Process Automation (APA)™ to improve performance.
A Big Data blueprint for success begins with a single question: What do you want to achieve? From there, apply a laser-like focus to your goals, translate them into well-formulated business questions, and watch the answers emerge from specific data elements.
Don’t Sink in the Data Swamp
But you know that’s not as easy as it sounds. Your data can come in various forms: Structured or unstructured. In-house, in the cloud, in partners’ databases, online, in social media. Real-time, near-real-time, non-real-time. Clean, smudged, or wallowing-in-the-dirt filthy. Fit for descriptive, diagnostic, or predictive analysis. For every line of business, there are large sets of external data elements you could layer onto your internal data to improve performance — demographic, geospatial, and firmographic. One set of data queries may only lead to the next, in search of the “right question” — or the right data to answer the question. Then, at the end of it all, there’s another long list of key performance indicators that measure the outcome.
The Right Questions, Asked and Answered
You can equip and empower your departmental staff to manipulate and extract meaning from this deluge of data, especially with self-service APA platforms. Paired with text-mining features that churn through unstructured data, assisted model building and machine learning that help you build models, and sentiment analysis features that can turn reviews and comments into actionable insight, APA platforms are designed specifically for the questions you have and the answers you need. With them, you can focus on questions such as, what are the key elements you should look for in your line of business in order to ask the right questions and arrive at the best answers?
While there is clearly no single data analytics blueprint for all lines of business — or even for all the questions that come up within your own line of business — there is growing experience with data analytics to learn from and mounting evidence of its role in driving performance and crushing the competition.
Fact:
Forrester reports that by 2020, insights-driven businesses will steal $1.2 trillion per annum from their less informed peers.
The McKinsey Global Institute paints an even starker picture, making these predictions about the performance gap between companies that fully absorb artificial intelligence (AI) tools in the next decade and those that don’t. AI adopters will double cash flow by 2020.
Decline in Cash Flow
There is an estimated 20% decline in cash flow expected for AI non-adopters by 2020.
Consider that, for department leaders like you, the two biggest external concerns are increased competition from the likes of those Big Data achievers and the changing preferences of your increasingly digital customers, according to a recent survey from the IBM Institute for Business Value. Let’s see how data analytics might address one department’s preoccupation with this second group — your customers.
Marketing Analytics: Sizing up the Customer
In marketing, the better you understand the individuals you’re communicating with, the better your chances of landing your message. Demographic data is crucial for communicating with a specific audience segment.
In a business-to-consumer (B2C) market, you need to understand more about your customers and prospects beyond their neighborhoods and names. Whether for sales and marketing, product design, or even packaging considerations, visibility into household income, age, marital status, and hobbies adds critical data you’ll want to have when segmenting audiences. You might also want to know the types of technology, food, and household products they purchase to increase your understanding and inspire new segmentation approaches.
In a business-to-business (B2B) market, you’re likely more concerned with firmographics. How many offices does the company have? How many employees? What are the industry, size, and revenue? What are your target’s yearly sales versus profitability? For packaged products, does the company use discrete manufacturing or process manufacturing? How big is its customer set? Is it B2C or B2B? Does it lease or buy its equipment Where is the company in its corporate lifecycle?
Firmographic segmentation, like other enriched data, helps you target your audience. If a target company has had a bad year, for example, it’s less likely to be interested in a generous new employee incentive program. This type of detail can help prioritize sales efforts and uncover which leads are more likely to pay off.
Supply Chain Analytics: Visibility Breeds Contentment
In another example of data analytics at work, it’s now possible to use analytics along the entire length of your supply chain. Accurately assessing and acting on supply chain data starts with this visibility. From the procurement of raw materials to production and distribution, you can analyze relevant data to ensure partners and customers are satisfied. For example, determining where there will be price volatility, or issues satisfying demand, can greatly reduce supply chain disruption.
Another important goal of supply chain analytics is to improve efficiency and forecasting, to be more responsive to customer needs. There’s nothing worse than not being able to meet a delivery deadline because your inventory is in the wrong warehouse across the country. Predictive analytics can also help anticipate consumer demand, which you can use to identify opportunities to save costs, adjust inventory, and accelerate delivery throughout the supply chain.
Every Question Tells a Story
Certainly, there’s no one-size-fits-all “right question” to ask in every department — nor a single set of data elements to answer it. But the following table provides some examples of data analytics at work, by showing sample questions for key departments across the organization and how you might frame the questions to get answers, act, and deliver results:
The Specs: Answering Key Departmental Questions with Big Data
Jump to a department:
Sales + Marketing
Key Challenge
Meeting sales goals
Elements of Success
Analyze opportunity data and sales team performance
Key Performance Indicator
Close ratio
Action
Analyze and break up bottlenecks in the sales cycle
Outcome
Improve resource deployment and build new revenue streams by aligning sales and marketing on the right customer profiles and targeted offers
Customer Service
Key Challenge
Customer experience
Elements of Success
Sentiment analysis
Key Performance Indicator
Customer satisfaction score
Action
Identify strategies to reduce customer churn
Outcome
Continue serving your existing customers, prevent attrition, and build stronger relationships by offering them the right product, at the right time, through the right channel, using the right message
Finance + Accounting
Key Challenge
Compliance
Elements of Success
Risk management assessment
Key Performance Indicator
Audit trail
Action
Conduct next-level analytic forecasting and model scoring
Outcome
Reduce risk exposure by performing more strategic tax, audit, and financial analytics, backed by a governed data pipeline that meets increased stakeholder scrutiny and global regulatory compliance
Human Resources
Key Challenge
Employee retention
Elements of Success
Flight risk model
Key Performance Indicator
Satisfaction level
Action
Predict which valuable employees are looking to leave
Outcome
Conduct compensation planning and review management styles to prevent employee attrition
Operations
Key Challenge
Efficiency
Elements of Success
Throughput
Key Performance Indicator
Unscheduled downtime
Action
Assess and activate operational and process improvements
Outcome
Respond to urgent analytic questions fast enough to take advantage of opportunities or fix a process before it becomes a business problem
The Takeaway
The business world has a Big Data conundrum — lots of data but nowhere near the wealth of insights we were all
promised it would deliver. Department leaders are looking to close that gap by putting data analytics to work on
their most pressing business questions. You want tangible results, weaving data insights into key performance
indicators to deliver a true impact on the business. And now, APA platforms for self-service data analytics
put that goal within reach.
Equipped and empowered, your own department could answer the majority of its own data analytics questions,
without the typical lag and lack of control found in arrangements for sharing resources like the corporate IT
department or data scientist. This could be a game changer for your department. Now, with more analytics control
within reach, what’s your blueprint for Big Data success?