What is Dark Data and Why Should We Care About It?

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What is Dark Data and Why Should We Care About It?

By: Linda Rosland

Mining dark data will be a trend in the coming year (Some, n.d.). The term “dark data” may conjure up images of nefarious web sites and activities, but in reality, it’s information that has been gathered through different computer networks, but is not used to derive any insights for decision making (Wikipedia, n.d.).

Dark data is like the detritus in an unorganized desk. There might be items of interest in there, but without categorization and evaluation, those items don’t serve a useful purpose.

Dark data is unconstructed and it’s formats not easy to analyze. Often there is so much of it that it’s hard to access. Many companies and organizations keep dark data “in case” they find a use for it (Tittel, 2014). Dark data can include text messages, log files, presentations, account information, documents, email, audio and video files and images (Kambries, Roma, Mittal, & Sharma, n.d.). It can also include service tickets, customer complaints, paper documents and the deep web, which is hundreds of time bigger than the surface web (Chandler, n.d.). All this data holds possibilities for being useful and has potential for positioning organizations to be at the head of the pack concerning data driven customer relationships.

Organizations keep dark data for different reasons. Storage is usually not expensive and not difficult, and dark data might be useful in the future. However, though it may not be expensive to store dark data, analyzing it can be costly and time consuming, two reasons that many organizations don’t actively pursue examining their dark data (Wikipedia, n.d.).

Not inspecting dark data at all or quickly enough, has consequences. Dark data may contain sensitive information and needs to be safeguarded to prevent security breaches. Security risks can be prevented or mitigated by looking at this data and determining whether it is of a sensitive nature or not.

It is likely dark data in some amount, will continue to exist, in some form. However, organizations should have processes in place and make a point of regularly auditing their stored information. This process should include purging unnecessary data and should provide a structure for organizing data so that in the future, dark data can be made useful. If an entity finds that it doesn’t want to purge old data, it should find a secure way of storing and backing up the data. Organizations should assess the potential value of their data, and data should be encrypted when stored (Tittel, 2014).

There are some companies that focus on retrieving dark data. Deep Web Technologies is one (Kambries, Roma, Mittal, & Sharma, n.d.). Federal scientific agencies and several academic and corporate organizations currently use Deep Web Technologies search tool (Kambries, Roma, Mittal, & Sharma, n.d.). Deep Web Technologies web site explains that they create custom search solutions. They’re search tool, Explorit Everywhere! searches, retrieves, ranks, categorizes and analyzes data from deep databases that are not accessible using general search engines (Explorit Everywhere!, n.d.). Among Deep Web Technologies customers are: Intel Corporation, BASF, Boeing, the National Library of Energy, George Mason University and Stanford University.

DeepDive is a project led by Christopher Re of Stanford University. The project is not under active development any longer, but still has an active community. Deep Dive is a data management system that extracts value from dark data (DeepDive, n.d.). DeepDive has spawned, a private, for-profit company., founded by Christopher Re and Mike Cafarella, builds on top of DeepDive. It converts unstructured data to formats that can be used by current data analysis tools (, n.d.).

As we place more emphasis on data driven customer experiences, parsing dark data will become vital. Organizations that are mining and consolidating dark data now will be ahead of the curve in the future.


Chandler, N. (n.d.). How the Deep Web Works. Retrieved from

DeepDive. (n.d.). Retrieved from

Explorit Everywhere! (n.d.). Retrieved from

Kambries, T., Roma, P., Mittal, N., & Sharma, S. K. (n.d.). Dark analytics: Illuminating opportunities hidden within unstructured data. Retrieved from (n.d.). Lattice io. Retrieved from LinkedIn:

Some, K. (n.d.). Top 7 Big Data Analytics Trends For 2019. Retrieved from

Tittel, E. (2014). The Dangers of Dark Data and How to Minimize Your Exposure. Retrieved from

Wikipedia. (n.d.). Dark data. Retrieved from

Future Data Centers

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Future Data Centers

By: Nicole Aiello

Data centers are the powerhouse of the internet. I would not be able to write this blog and you certainly wouldn’t be able to read it if it wasn’t for the multitude of data centers’ network of building systems, telecommunications, computer systems, storage, and related mechanisms. It’s no surprise that data centers are “energy guzzlers that can demand megawatts worth of electricity to operate” (Nelson, 2017) as Network World’s Patrick Nelson put it. A company can reduce their overhead costs by implementing self-sustaining energy sources for their data centers. A strive for a greener data center doesn’t stop at zero intake – zero waste is an emerging trend in this technology as well. According to TechRepublic (Shalcklett, 2018), 2019 will launch data centers into a “greener” and more self-sustenance era. Last year Aruba set precedent for future data centers by opening the first self-sustaining data center named the Global Cloud Data Center (ID3) in Milan, Italy. Setting the competition bar highest this year is Apple who announced in April of this year that it is now 100% powered by renewable energy (Apple, 2018). Their “data center has been designed to make the most of every possible solution to reduce its impact on the environment as much as possible,” the company says. “It is also supplied with 100 percent renewable energy from sources certified by the European Guarantee of Origin (GO).” (Nelson, 2017) Google has followed suit and constructed a solar farm to sustain their data center in Belgium reaching 100% sustainability in 2017 says ZDNet’s Steve Ranger (Ranger, 2018). With these tech giants putting their green thumb forward, their data centers are able to run with almost complete automation. Additionally, they’re able to feed electricity back into the public power grid making their sustainability efforts a public benefit. Falling in line with trend, Milan’s data center touts being “zero-impact” by utilizing geothermal cooling, hydroelectricity, and solar electricity. A data center capable of producing its own power creates redundancy and independence which increases is reliance. Australia is following suit and has announced its plan to build the countries first data center “powered primarily from renewable energy sources” (Nott, 2018) to include a solar farm writes Computer World’s George Nott. This data center will be juxtaposed in the coal-mining town of Collie. By utilizing geothermal cooling to pump cold water up to cool and returning that now warmed air back to the ground the data center in Milan is able to reduce their environmental impact to near zero.

The future of data centers looks green. Moving forward, sustainability attempts include measures related to:

  • Solar
  • Biogas fuel cells
  • Battery storage
  • Providing energy back to the public grid
  • Wind
  • Magnets and magnetic components


Apple. (2018, April 10). Apple now globally powered by 100 percent renewable energy. Retrieved December 14, 2018, from Apple:

Nelson, P. (2017, October 18). First self-powered data center opens. Retrieved from Network World from IDG:

Nott, G. (2018, August 9). Solar powered data center proposed for Collie, WA. Retrieved December 14, 2018, from ComputerWorld from IDG:

Ranger, S. (2018, February 2). Google builds out its data center estate, with added solar power. Retrieved from ZDNet:

Shalcklett, M. (2018, December 10). 8 emerging technology trends you can expect to see in 2019. Retrieved December 14, 2018, from TechReplublic:

Influencers in Social Media

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Influencers in Social Media

By: Franklin Kuzenski

What is it worth when a micro-influencer wears my logo?

Social media analytics is measuring the effects of social media on your business (Thomas, 2018).

Social media listening is scanning social media to learn the reach and sentiment of your brands (Boyd, 2017). It is a qualitative way to discover the power of your brand on social media. Listening also encompasses looking for your company and product names in hashtags and message bodies and searching for competitors and terms related to your industry. Social media listening doesn’t have to be passive. You can respond directly to comments and questions about your product. You can spot and react to a crisis or perform customer service. A trained marketer can see how people are reacting to ad campaigns and events.

Social media monitoring is quantitative tracking keywords and conversion statistics to measure the success of campaigns over time (Thomas, 2018). Social media monitoring is systematic compared to listening. Social media monitoring analytics tools go beyond mere brand mentions (Perzyńska, 2017). They count the likes, shares, comments, etc. Social media monitoring empowers ad campaigns. Paid or free tools can tap databases of social media activity to reveal micro- and macro- conversions and the demographics of your audience. By deploying trackable links to share, analysts can dissect their performance and tell you the lifespan and reach of a post (Parker, 2017). With that information, you can make predictions and act on insights.

A social media influencer is a person with an audience of followers that can achieve results through imitation or direct call to action (Lieber, 2018).  

An influencer can be hired or convinced to post about a product or service. Influencers can be recruited after social media listening or social media monitoring identifies them. Some people make a living as social media influencers (Milokhina, 2017). They publish content that attracts an audience by offering advice, entertainment, news, or something else that gets attention. Other influencers may not realize how popular they are or how to obtain value from their situation (Lieber, 2018). A marketer can use tools to measure a potential influencer’s reach or run experiments by having them post something and seeing how far it spreads. Being an influencer is not a permanent status. If the influencer stops delivering interesting content or alienates their target audience, their value will drop (Jade, 2016). Audiences will develop “banner blindness” if paid copy overwhelms their native content (Businessese, 2016).

Adapting analytical tools to track an influencer’s return on investment (ROI) can be challenging.

Successful influencers tend to post videos, emojis, or pictures (Chahal, 2017). Their followers can’t be relied upon to use the correct hashtags as they spread your message (Roach, 2018). Artificial intelligence can decipher spoken audio and imagery through tools called visual analytics (Albane, 2016). Visual listening extracts value from the results by examining the context of the appearance or mention and forming insights (Chan, 2017).

A new trend in social media influencer marketing is cultivating micro-influencers (Farmiloe, 2018).

A micro-influencer has a smaller audience than a celebrity but has enough trusting followers to make it worthwhile to form a relationship with. Micro-influencers have a niche, modest-sized audience, not wide appeal or a massive following (Barker, 2016). A quality micro-influencer has a closer relationship of trust with their followers. They’re in a position to respond to almost every remark and tailor their content. You can expect a much higher conversion rate from social activations (Farmiloe, 2018). If your firm can’t trade quantity for quality and corrals a stable of micro-influencers, there are marketing relationship management tools to track multi-source conversations and commissions (Criterion Global, n.d.).

Influencers of all sizes often produce other online content like blogs, podcasts, and mobile apps.

Social media marketing technology is steadily advancing. The field is ripe for new strategies to monitor engagement that goes beyond likes and shares (CVC Spin Off, 2018). Firms are still seeking the perfect way to measure the impact of their logo if it were visible somewhere such as in an Instagram Boomerang or the me_IRL subreddit.


Albane. (2016, May 26). Visual analytics: Find your brand’s visual influencers. Retrieved from

Barker, Shane. (2016, December 1). How to find social media micro-influencers for your small business. Retrieved from

Boyd, J. (2017, October 10). The complete social listening guide. Retrieved from

Businessese. (2016, September 8). Retrieved from

Chahal, M. (2017, February 22). The impact of image-based posts on social media insight. Retrieved from

Chan, M. (2017, September 27). What is visual listening, and why is it a must for marketers? Retrieved from

Criterion Global. (n.d.). Social Strategy. Retrieved from  

CVC Spin Off. (2018, April 4). Visual tagging: Visual listening for social networks. Retrieved from

Farmiloe, B. (2018, August 23). The power of micro-influencers on Instagram. Retrieved from

Jade, Z. (2016, October). How many fans does it take to become a profitable influencer?

Lieber, C. (2018, November 28). How and why do influencers make so much money? The head of an influencer agency explains. Retrieved from

Milokhina, K. (2017, December 12). How to track Instagram referrals and paid traffic in Google Analytics. Retrieved from

Parker, S. (2017, April 26). What’s the difference between social listening and social monitoring? Retrieved from

Perzyńska, K. (2017, June 13). Social media monitoring vs. social listening. Retrieved from

Roach, A. (2018, December 2). The ultimate guide to the best Instagram hashtags for likes. Retrieved from

Thomas, M. (2018, October). The Financial Times guide to social media strategy. Retrieved from