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We â€“ technology stakeholders, executives, consultancies and analysts â€“Â are wrong about enterprise IT.
Since the early days of the internet boom, enterprise IT has been criticized for not being responsive, transparent, flexible, and innovative. We believed that leadership, organization, workforce, culture, and vendors were the culprits. We were convinced that a fix had to do with elevating the CIO role, acquiring new skills, outsourcing tasks, aligning strategies, knowing customers better, and running IT like a service company. After so many years of religiously applying these recipes, enterprise IT is still facing the same criticism. Moreover, there is a growing trend of taking technology spending away from enterprise IT into the business and out to the third-parties with a hope of stimulating innovation, improving agility and reducing costs.
With bitcoin crossing the $4K mark and the market cap for cryptocurrencies currently residing around $140B, thereâ€™s a definite sense of a gold rush in progress much like the dot-com era and the real California gold rush of the 1800s.
While parallels with the dot-com era have been well reported, especially related to exploring what phase of the bubble weâ€™re in, thereâ€™s been relatively less comparison with the California gold rush. So what can history teach us from the physical gold rush that may guide us in the midst of todayâ€™s digital equivalent? To help address this question, hereâ€™s some parallels between the two and some recommendations for organizations in terms of charting your course.
Back to school isnâ€™t just about yellow buses, backpacks and pencils. For colleges as well as the cities and businesses that surround campuses, it means an influx of students and dollars. The fact that just about all of those people will be carrying mobile devices means thereâ€™s far more data available than ever before that can help local businesses get a better handle on whoâ€™s in town and uncover potential opportunities.
When my firm UberMedia evaluated Washington, D.C.â€™s Georgetown neighborhood, home to schools including Georgetown University and George Washington University, earlier this year, we used data showing the pathways to well-known shops there such as Anthropologie and Banana Republic. Anonymized mobile location data can tell us a lot â€“ where people come from, how long they spend in each location and where they travel afterwards. Even in that simple form without additional data layered on, itâ€™s powerful information for these large international retailers.
Historically, the race for an edge using big data was about piping in as many data feeds as humanly possible and getting access to that data as close to real-time as possible. A lot of time and investment has been spent solving API access and data latency problems.
As we dig one level deeper, however, it would appear weâ€™ve focused entirely too much attention on the accumulation and storage of the sea of big data and not nearly enough time ensuring the full accessibility of that data.
According to Forrester, less than 0.5% of all data is ever analyzed and used. And yet, in a recent webinar hosted by InfoTrust, Richard Joyce, Senior Analyst at Forrester, said that â€œJust a 10% increase in data accessibility will result in more than $65 million additional net income for a typical Fortune 1000 company.â€�
As a former product manager, I was curious about whether the concept of A team versus B team existed within IT organizations as well. To facilitate this #CIOChat, I told participants about a person that I met at Apple several years ago. This personâ€™s business card read â€œNewton Scapegoatâ€�. I obviously asked, â€œwhy would someone take on such a title?â€� The person said that Appleâ€™s A team was spun off as General Magic. For those that do not remember, General Magic developed many of the concepts of what mobility is about. According to the â€œNewton Scapegoatâ€� when Scully did not get the power of mobility, he spun General Magic off. But soon after doing this, his opinion changed. He then scrounged around for B players and gave them a sadly impossible agendaâ€”doing really hard things like hand writing recognition. As you either remember or can guess, the B team failed miserably with an impossible agenda.
At SecureWorks, a Dell Technologies company, we built a lab over a 15-year period that contained more than 500 physical and virtual servers. Our software development teams use the lab daily to design, develop, test and deploy critical applications to protect our customers.
Due to company growth and expansion and with minimal interruption to the teams that use the lab daily, we needed to relocate the lab across the country to our new Software Defined Data Center (SDDC).Â We were given 90 days to complete the move. Â Here we share what we did to meet the deadline, to work with other teams and to garner support from management.
What is a data scientist?
Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role in data analysis is becoming increasingly important as businesses rely more heavily on big data and data analytics to drive decision-making and as more businesses lean on cloud technology, automation and machine learning as core components of their IT strategies.
A data scientistâ€™s main objective is to organize and analyze large amounts of data, often using software specifically designed for the task. The final results of a data scientistâ€™s data analysis needs to be easy enough for all invested stakeholders to understand â€” especially those working outside of IT.
The Internet of Things (IoT) ecosystem is changing the way people live, work, and even vacation. Itâ€™s also affecting the way businesses communicate with each other and the world around them. A few examples of changing business environments are: smart sensors for manufacturing, agriculture, and oil and gas; connected cars; beacons paired with mobile apps and digital signage for the smart store and smart restaurant; home devices connected to the Internet; smart cities; connected hotels and airlines; and innovative technologies that are improving healthcare.
Intelligent automation technologies, including robotic process automation (RPA) and artificial intelligence (AI), offer transformative opportunities for companies to shift the ways organizations do everything from running operations, moving through the supply chain and serving customers. Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
But making decisions about digital labor â€” that is, an automated workforce with capabilities to complete work that largely mirrors our own abilities â€” cannot be taken lightly. These efforts can have enormous and lasting effects on your workforce, on communities and on the entire world â€” so they require significant thought and preparation, including digging into a companyâ€™s deepest core values.