In day-to-day life, there are numerous things all around us that have value but go unused. Consider that in 2014, $750 million in gift cards went unused, the American worker forfeited 169 million vacation days and an estimated $165 billion in food went to waste. By some estimates, 90% of the average human brain goes unused.\nThese measures offer a nice segue into a challenge that many organizations face today in their analytics efforts \u2013 uncovering, leveraging and making positive use of \u201cdark data\u201d\u00a0\u2013 that is, data that resides in the organization but is not used effectively or, in some cases, at all.\nExamples of dark data can include audio files of conversations with customers, complaint emails and chat messages, data captured in bank transactions, digital marketing campaign information, email click data, and so on. Additionally, an organization will often have data on the same individual from multiple relationships with the customer, but will lack the ability to map, cross-reference and arrive at a holistic and more complete picture of the customer.\nHow do organizations wind up in a dark data abyss?\nLack of awareness:\nIn many cases, the functioning members of the organization are simply not aware of the existence of this data. In the case of a bank, for example, an underwriting team may see information provided by a customer on an online credit card application form and collect it as valuable data. But they may not know that data on the customer\u2019s journey to get to that point \u2013 how they ultimately arrived at the application page\u00a0\u2013 is available as well. Either as a result of communication deficiencies or absence of training, dark data issues can arise when the availability of data is simply unknown.\nDisconnect among teams:\nWithin larger organizations, data is often collected separately by different business units or teams. And once collected, that same data is often owned and managed by separate teams. There is usually no natural mechanism for data sharing between teams and it becomes an uphill task for one team to get hold of and understand data from another. A pool of data that may not have use for one team may be of great value to another, but the necessary sharing just does not happen.\nTechnology and tool constraints:\nThe manner in which data is collected and the disparate nature of the technology systems deployed within an organization can also lead to a dark data problem. Because collected data sits in separate silos, it is often difficult to systematically bring it together to produce a clear, cohesive picture. This is especially true for companies with legacy IT systems and where systems and IT formats are different (think audio files from call center interactions or click data from Web platforms). And it\u2019s a common problem faced by those in the early stages of adopting a data analytics program.\nCreativity gap:\nEven if teams are aware of the availability of different types of data, have access to it, and have the tools to use it, they may still not end up using this incremental data just because it is new to them. Great Data Scientists are creative and have a hunger to learn \u2013 they are able to come up with new and interesting ways of using all data assets available to them. If this drive towards innovation is missing in a team, the organization might still have patches of dark data.\nHowever ominous and spooky sounding the term \u201cdark data\u201d may be, there are steps organizations can take to extract this valuable data and put it to good, business-enhancing use. Here are some ways of achieving this:\nA new function:\nEstablish a central data analytics function to be headed by a Chief Data Analytics Officer. It would be this person\u2019s responsibility to obtain a complete view of the organization\u2019s data assets and determine how best to share across teams and serve multiple functional areas. While executive teams may be leery of creating another cost center inside the company, the benefits to the business will far outweigh the costs in short order.\nAudit of IT tools:\nAs explained above, an organization\u2019s existing technology infrastructure may be the primary gating factor to data sharing and usage. Do your legacy systems prevent you from combining disparate data elements? Do you have speech analytics tools to use the information hidden in audio files? These are just a few sample questions. You need to determine whether your current systems are compatible for use with your analytics programs and aspirations. If not, make a business case for investment in IT tools by sizing up the opportunity through a pilot.\nCodifying data models & dictionaries:\nDetermine if appropriate data model documents and data dictionaries are created and maintained in the organization. Also, check if everyone in the organization is speaking the same data language and if that language is easily transmittable and understandable across teams, with new team members and is not lost through employee turnover or attrition. Data descriptions and dictionaries need to reside in clear documents, not in people\u2019s heads.\nExtracting value from dark data can be complicated, but it is not an insurmountable task. For those organizations in the early stages of their analytics efforts, it does not need to be an immediate priority. But for those organizations that have already completed some of the basic steps of analytics and have begun initial level predictive modeling, the dark data challenge can be a useful next frontier that can help the company derive more value from its analytics program.