What is Data
Wrangling?

Data Wrangling is a new mode of data preparation and integration that is more agile and expansive than traditional ETL and data warehousing. Wrangling requires new tools, techniques and even a new mindset to deal with today’s data management challenges.

Wrangling your data pulls it together, cleans it up, and positions it to drive real business value. Data-driven initiatives within sales and marketing are especially well served by wrangling because the variety, volume, velocity and value of commercial data sources is ever expanding.

Complete Customer Knowledge

The proliferation of digital touch points has led to an explosion of sales and marketing apps such that most enterprises have dozens of applications in their front-office portfolio. Best-of-breed is the only way to keep up, but despite best efforts to integrate, most systems produce isolated and incongruent data. Companies have long tried to ETL their customer data into BI warehouses. But proliferation of the count and categories of data sources, including 3rd party unstructured files feeding exploratory ML and optimization apps, classic data management has fallen further behind the needs of business. Wrangling is the only way to deal with the rush because it accommodates ever-changing inputs and outputs using an agile but still systematic approach.

Process Data at the Speed of Business

Analyzing customer behavior as monthly aggregates is no longer enough to compete and win. You need to know what customers, and competitors, did today. Soon, you’ll need to know what they are doing right this minute. The new standard for commercial data timeliness is real time. It’s the only way to optimize sales operations and support agile marketing tactics. Wrangling can transform compressed marketing and sales cycle times into a source of competitive advantage. Tuning marketing messages and product recommendations based on current actions, automated lead triage, and dynamic pricing are just a few examples of commercial best practices that require precision data inputs in near real-time.

Empower your Entire Front Line

Marketing and sales teams are ideal candidates to become citizen analysts because they’re always searching for information advantage. Equipping them with modern tools like Tableau can be highly productive but only if you give them clean, prepped, relevant data to work with. But if you feed them raw, messy data, they will try munging it themselves until the realize it’s a waste of time. Wrangling is the key to unleashing their collective knowledge and creativity and gaining their buy-in on data-driven decision making.

Common Commercial Wrangles

Commercial Data Excellence ensures that investments in front-office applications and analytics platforms pay off, and that their value grows over time. Here are several examples where data wrangling can make or break a data-driven initiative.

Pricing Analytics & Optimization

Comprehensive pricing, sales and cost data is crucial to performance measurement and process optimization. Pricing and profit analytics are so valuable that many companies are implementing stand-alone pricing software and AI. But real-world order processing and eCommerce typically produce dirty transactional data with minimal customer, product context. Firms eager to adopt pricing best practices like profit waterfalls, customer-level profitability and optimized guidance struggle to feed pricing software the rich, reliable data it requires.

Customer Segmentation & Profitability

The age-old CRM vision of a 360-degree customer view is as compelling and elusive as ever. Interaction channels and touchpoints have exploded requiring data integration and alignment across countless social and transactional marketing and sales systems. The latest iteration on C360, “Customer Data Platform,” is touted as the path to omni-channel excellence but requires the dynamic integration and analysis of ever-increasing, highly varied structured and unstructured data sources.

Merger & Acquisition Data Integration

The consummation of M&A deals are exciting and stressful events. You want to tap into the synergies as fast as possible, but that requires getting the commercial organizations aligned and up-to-speed on new products, customers and approaches. Post-merger sales enablement requires rapid consolidation of data, systems and processes, with the data serving as the most tangible lynch pin between old ways and the new. Commercial data must be prepped fast and accurately, or the entire business case is at risk.

Data Prep for Machine Learning

ML / AI projects are being launched in droves, with especially high hopes related to enhancing customer experience and sales results. Whether they will deliver the expected value remains to be seen, but it is certain that the quality of the data inputs will be a key factor. Machine learning algorithms are best tuned with diverse data that is often discovered through experimentation. While many data scientists can prep data themselves, is it really the best use of their time and expertise? Given the shortage of experienced data scientist, minimizing the time and effort they spend wrangling can make your data science programs much more productive.

Product Portfolio Management

Achieving the full market potential of a product requires a holistic view of its customers, costs, profitability and channel mix. Pulling this information together across your complete portfolio requires tying product master data with ERP, transactional and CRM data. Product managers that do this work by hand (in excel) may spend more time munging than they do managing.

Channel Sales, Promotions & Rebate Tracking

Channel sales can provide critical market insights and deal validation and is thus required of distributors by many manufacturers. The dirty secret is that most POS is little used because it’s too complicated to match customers, products, quotes and orders. Without closing the loop, channel inefficiencies and blind spots can compromise an otherwise strong channel program.

Contact us today to learn more about our data wrangling and data management services.