Time to Stop Talking Around Big Data, Embrace It and Drive Your Business Forward

Author: Jessica Hawthorne-Castro, CEO
Original Link: Digitizing Polaris
Date Published: July 21, 2017

One of the most pressing challenges facing modern businesses today is shifting culture towards a data scientist-driven approach. To complete this shift, a company doesn’t just need a big data strategy, it needs a business strategy that incorporates big data in a way that empowers all employees to think like data scientists.

Developing the Data-Driven Business

For businesses, “big data” means taking information from various disconnected sources and using analytics tools to combine it and find new insights. It’s about correlation, discovering connections between events or information that were previously hidden

For the modern company, data is the most important asset, one that is dynamically changing. Valuable data sets come into play every day from mobile-based customer interactions to the billions of internet-connected “things” coming online. The trick for companies is to develop smart strategies to leverage this flood of data and to monetize data while improving customer loyalty and overall experience.

Three Tactics to Become a Data Driven Company

Think Differently

To think differently, you must create a business strategy that focuses on data, one driven by the leaders of the organization. Adopting a big data approach requires a cultural shift within all levels of the organization. For example, training is crucial for the adoption of big data-focused analytics tools. Staff members must understand how to look at large data sets and pull insights, whether they are based on customer behaviors, problems in the supply chain, or adjustments that could be made to operational processes.

The training needs to include actual sessions with the technology to showcase the ease of use and how it relates to the specific individual’s role within the company. It also needs to be backed by management and include a shift to a more of a real-time and cross-departmental structure. This requires management to loosen some of the constraints of the traditional business hierarchy, where previously insights had to go up a lengthy chain of approval or review. There are still checks and balances, but there is a shorter cultural pathway from finding out something is wrong to implementing a data-driven fix.

Another cultural change involves ceasing to make decisions and taking action based solely on “gut feelings.” This doesn’t limit intuition or innovation, but it mitigates risks and uses hard data as a way to augment creativity and decision making. It’s a model with built-in accountability as staff members need to justify their recommended actions with data.

Make Real World Changes through Data

Companies need to make dramatic data-centric shifts because of the real, impactful changes it can produce. Big data will drive change and induce innovation in your company as well as spur growth, improve agility and foster communication. And data is the most important asset to monetize drive loyalty and safeguard privacy and security.

Consider these examples of the direct impacts of a big data approach:

  • Review financial data to uncover incidences of fraud or potential risk
  • Refine a product’s attributes based on customer-derived data and sales numbers. For example, analyze how customers run on an IoT-enabled treadmill and adjust its construction accordingly.
  • Analyze target markets to see if demographic parameters are off-base and make adjustments
  • Enhance the customer experience by offering deals and retention strategies that are backed by data insights
  • Develop surprising new marketing initiatives and targets through review of sales data combined with customer service data, and other sources

For an industry-specific example, the advertising industry is rapidly changing due to new streams of data. Advertisers and broadcasters crave detailed metrics on viewer behaviors, they are now receiving new details via “second screen” behaviors. A significant number of consumers utilize their phones or tablets while watching television and their actions during this time provide advertisers with rich data about likes and dislikes, which can then be used for more targeted advertising campaigns.

Build a Big Data Strategy

In order to reach the goal of a data-driven company, every firm needs a big data strategy that breaks from conventional thinking. This is a whole business initiative, not an IT initiative; however, IT should enable your result. IT should be at the lead of the implementation as they control the information, but multi-departmental cooperation is a must. Remember the business goals always comes first. Develop a sound business strategy that incorporates data as a core element, with a focus on how staff members make decisions that ultimately impact the business. Augment these decisions with rich and multi-sourced data, and you’ll create a company that can move with agility beyond its competitors.

Here are some of the key steps for creating a data-driven company:

  • Assess pressing business needs. What are the unknowns about products, customers, and internal processes?
  • Map out your data architecture and identify all data sources. Can you pull together customer service chat transcripts along with website visitation metrics? You need disconnected data sets.
  • Determine how to consolidate and relate all of the data sets.
  • Develop user-friendly solutions to analyze the data to help you find surprising insights. Data should drive discovery. Let users play with the information in new ways. Rigid Excel reports and big data analytics do not mix.
  • Turn those insights into actions.
  • Consistently look to bring in new data sets.

There are some of the caveats of the big data approach that companies should be cognizant of so that the can make informed strategic moves. An over- reliance on big data shouldn’t remove personal communication between the company and consumer. And, while there are many correlations that can be uncovered through big data analysis, there’s still a human element needed to confirm that the correlations make contextual sense to the business. It’s still an “art and science” approach, ideally one where the data makes decisions better and faster.

And don’t worry if you are just entering the big data game. The truth is that cost effective data management wasn’t available until recently, so it is not unusual to have a ton of data in many, many different places. As you begin, make a goal of centralizing your data so that scalability to meet the needs of your business is less complicated down the road. The amount of data gathered increases exponentially; so, you’ll be glad you started now.


Jessica Hawthorne-Castro is the CEO of Hawthorne Direct, a technology-based agency specializing in analytics and accountable brand advertising. She has strategically positioned the agency to be at the forefront of the marketing paradigm where art meets science. Throughout her tenure with Hawthorne Direct, she has fostered long-standing relationships with the company’s expansive base of diversified clients resulting from an unwavering commitment to unparalleled service. Her role principally involves fostering long-standing client relationships to develop highly strategic and measurable advertising campaigns, designed to ignite immediate consumer response.

5 Ways to Pull Order and Insights from Big Data

Author: Jessica Hawthorne-Castro

Original Link: Datanami

Date Published: June 1, 2017

Walmart is a global leader in using data analytics to form insights. For example, the company previously used data generated during hurricanesto better estimate demand for emergency supplies, so it could be best prepared to meet customer needs. The company focuses on both reactive and proactive analytics, so they can thoroughly review past problems and spot upcoming errors in advance.

Enterprise companies such as Walmart that are successfully using Big Data has some commonalities. They have the technical expertise and the right systems in place to manage massive sets of data and know how to learn actionable insights from the data. For marketers that want to improve their results through data analysis, it can be tricky to correlate what the data is saying and how that could adjust marketing strategies.

Here’s five best practices companies can employ to refine their campaign performance through big data:

1. Leverage Pixel Tracking

Marketers should not overlook their company’s websites as data sources that provide them with a wealth of information about customer behaviors. A primary method of capturing website data is through pixel tracking, which can be placed on mobile sites, landing pages, and any other web-based channel. Marketers can also track social media with pixel tracking to better understand Facebook ad performance and the visitation patterns of followers. Pixel tracking not only provides information on what visitors click and how long they stay on certain areas, it can also provide device data. Marketers can then tailor campaigns, by for example matching certain product lines with mobile customers.

The broader point of having this trove of data is to create “profiles and personas” of groups of customers. Armed with such profiles, marketers can then correlate behaviors with buying decisions or indecisions. For example, they might see a longer “mouse hovering time” with certain products, and could then propose pricing adjustments or added incentives to make those products more enticing.

2. Use the Cleanest Data

Big data is only as good as the underlying data – it’s not a magical technology that can fix sloppy data. If a company pushes poorly organized data into the analytics solution, then the resulting insights will be unreliable even if they are pulled by an expert data scientists. Marketers should demand that data going into the system will be scrubbed and organized. This does not mean data streams should be restricted, they should just be well constructed and reliable, and so the end results are meaningful and have real value.

Proactivity is crucial, and companies should put in the work on the front end to improve their data quality instead of attempting to make changes during active campaigns.

3. Employ Statistical Modeling to Predict Future Results

TV campaigns can be improved by using data collection to look at past results, and then making the right tweaks for the future. Marketers must have access to metrics on demographics, second-screening, airing size, and Nielsen weighting points in order to build statistical models.

Clean and granular data is essential in order to inform regression analysis, logistic regression, and cluster analysis to dive deeper into campaign performance. TV campaigns do not have to be unmeasured branding boosters. With analytics, agencies and marketers can show measurable results from TV.

4. Improve Demographics Targeting

Rich big data is ideally suited for targeting. It gives marketers the info they need on the location of customers, the devices they are using to access content, their behaviors while on social sites, and even their search habits. Pooled together, marketers can develop correlations, such as relationships between devices and products, or location and preference for mobile or landing page. Marketers must have the right tools in place that allow them to adjust future campaigns based on the improved targeting, so they can present adjusted granular messaging.

5. The Benefits of Mixed-Media Modeling

Mixed media modeling entails careful analysis of both sales and response data. Marketers can then review various channels and eliminate laggards while pushing more budget towards the top performers. This process will improve over time, as the data becomes cleaner and more structured, and marketers and data scientists will improve their analytical talents. Such modeling is especially important in the fragmented media landscape, as there are many choices for marketers and they need to narrow down the best channel prospects.

Using big data analytics to improve ROI is a necessity for today’s modern multi-channel business. Success with big data takes a measured approach, where marketers are focused on pairing insights with past and future actions. Big data provides marketing with a much-needed measurement tool that can use to prove their result and improve their campaigns year-over-year.

About the author: Jessica Hawthorne-Castro is the CEO of Hawthorne, an award winning technology-based advertising agency specializing in analytics and accountable brand campaigns for over 30-years.  Hawthorne has a legacy of ad industry leadership by being a visionary in combining the art of right-brain creativity with the science of left-brain data analytics and neuroscience. Jessica’s role principally involves fostering long-standing client relationships with the company’s expansive base of Fortune 500 brands to develop highly strategic and measurable advertising campaigns, designed to ignite immediate consumer response.