Big data initiatives have become a top investment area and a strategic priority for forward-thinking organizations in many different businesses.
Different industries leverage big data analytics to assess and respond in real time. From retailers who gain deeper understanding of customer behavior and preference, who can segment customers in new ways, and can approach targeted potential customers with personalized offers, to financial service companies using data to detect and prevent fraud.
From manufacturers using data to optimize their supply chain and anticipate problems, to energy companies that use big data to anticipate demand and optimize production. From healthcare to research and government agencies who are exploring data for intelligence and national security, research and development – all rely on big data.
Data analytics offer B2B companies an unmatched opportunity to gain business insight and make smarter decisions. When it comes to marketing and sales, B2B companies have distinctive needs, strengths, and weaknesses working with big data. The goal is still the same – drive growth in a profitable, predictable, and repeatable way – but unlike in the beginning of 2000’s, when the problem was not having enough data, today’s problem is having too much of it.
There is a huge amount of data coming in from different source: contracts, customer communications, call center transcripts, social media, industry information, documents, emails and so on. Sometime the information comes from outdated platforms or a mesh-up of old and new programs that not always work together properly. This data puts a dent in the budget but rather than meeting the company goals, it creates information overload – physical and intellectual, confusion, and ‘busy work’.
“Data without insight is worthless, but where do you find the points of tension and points of interest and use that as a way to inspire creativity?” asks Leo Burnett in a recent interview in AMA, talking about the future of data and creativity as a competitive advantage.
That is a big question that touches areas that range from technical inconsistencies to keeping the staff up to date with training. The result? According to Dun & Bradstreet’s “The B2B Marketing Report”, “41% of B2B marketers surveyed cite inconsistent data as the biggest obstacle to maximizing ROI”, and 39% said their biggest problem is integrating technologies.
“Whether it’s improving customer experience, launching new offerings to existing clients or creating demand with new prospects, one constant undercurrent determines the success of these activities – the ability of the organization to access and derive meaning from the amazing amount of data now powering the systems that execute them. In B2B marketing, the accuracy and precision of the prospect and customer database which sits at the core of these technologies is what determines whether or not CMOs will meet the expectations put forward in the board room.” Dun & Bradstreet report says.
The amount spent on big data infrastructure, software, and services reached $16.6 billion in 2014, and this number is expected to grow to $41.5 billion by 2018 — a compound annual growth rate of 26.4%.
Good information governance is essential to the success of big data analytic projects. The quality of the data input will determine the trustworthiness of the analysis.
Most companies already manage their internal data effectively, be it CRM (customer relations management), HCM (human capital management), and SCM (supply chain management). The problem is with information that is not structured to one of those platforms.
Research has shown that this unstructured information accounts for about 90% of the company’s incoming information, and many companies do not have processes and systems to effectively manage this information throughout its life cycle.
The volume, the variety, and the velocity of the information is overwhelming and will continue to grow at a fast pace speed. Keeping the data clean and consistent will have to become a constant practice.
Metrics should be in place to manage information quality, how timely the information is, and its accuracy through the collection, monitoring, and management processes.
Finding and sorting the information from different sources on different systems, finding duplication, confidential and sensitive information, and organizing this information in such a way that toxic, out of date, and low priority information can be deleted. Reducing the clutter save on storage costs and makes it easier to discover high value, relevant information
Undoubtedly there will be changes in the future as far as the platforms go. Data will need to be transferred. Keeping it ‘clean’ will make changes easier.
There will be things a team would want to add, or there might be a change in strategy – building the team infrastructure based on clean data that is accessible and actionable will help transfer from one platform to the other when the time arises.
Using bad data for analytics and decision making puts a company at a competitive disadvantage and may expose the company to risks. Making sure the accuracy and truthfulness of the information that is fed into the big data has to become a top priority.
“Striking the balance between disposal and preservation — between minimizing clutter and risk and optimizing the potential for business insight through big data analytics — requires a deep understanding of enterprise information assets and an effective management strategy that factors in the needs of a very diverse set of stakeholders. Without the right information governance solution, this is an impossible task.” Writes Melissa Webster in her white paper called “Big Data, Bad Data, Good Data: The Link Between Information Governance and Big Data Outcomes”
Companies can assess their big data practices by asking these questions:
- Is there visibility into all the information, regardless to where it is stored? Sites, shared drives, email systems, and cloud applications?
- Is it difficult to identify high value information?
- Are storage and backup cost escalating and the volume grows?
- What is the security level for sensitive information in the company? Is it at risk for inadvertently disclosing confidential information?
Sometimes the team organization can get in the way of efficient use of data. Data might live on the computer or the cloud, but the people are the ones who make use of it.
Understanding what are the exact needs of the different departments using this data is paramount. Training should focus on using data to make better decisions rather on specific tools and techniques to help employees approach problems from a more pragmatic point of view. There may be instances when teams can shorten lag time in communication or improve consistency.
Here’s an example: Halliburton worked to improve communication between its global sites when an analysis showed clusters with few ties between them. Based on that they decided to create mixed project teams, rotating experiences individuals to other platforms and created an electronic expertise locator. Less than a year later, connectivity had increased by 25% and productivity by 10%. Customer dissatisfaction decreased by 24% and new product revenue increased as well, by over 20%. The improvements were attributed to the ability to share information and make decisions more efficiently.
What are the data points, data sources and KPIs different teams work with? Having a visual representation of the whole sales funnel and seeing where data points intersect with steps in the sales funnel will give an overview of the whole process.
Helping marketing and sales teams identify where there is useless data, and where there are gaps in the sale process which can use more data will make the process more streamlined.
Inconsistencies in data happen when there are too many hands stirring the pot. Multiple platforms, trying to measure a single metric in different ways by different platforms, or people messing with the same data source without communicating with each other – all those create confusion and inconsistency.
Changing from an experience based decision making process to a data driven decision making is not as simple as increasing the company’s analytical abilities. The way the problems are viewed has to be different. The outcome of a big data analytics project will be only as good as the quality of the data being used. Just having more data will not accomplish anything without deep understanding of the company needs and data points.