Identifying emerging, or even established, trends in information management is hard. Part of the problem is that information management is such a wide-reaching term that covers all kinds of data management, from ‘first-mile’ capture to archiving and destruction. However a number of common themes run through all the different enterprise technologies in the information management space — from artificial intelligence (AI) to machine learning to digital workplace to records management to document management to enterprise content management. Information management, as a discipline, covers all these different technologies in all contexts. The common denominator is how enterprises capture, manage and use the information and data that is needed to run their enterprise applications.
For the past five years, a massive data explosion has left IT departments reeling and struggling to develop strategies to keep it all secure and under control. Organizations are extending their partner network to third-party vendors to manage the massive influx of information, and while that helps alleviate the data volume challenge, it also presents new security obstacles. For organizations to stay ahead of third-party threats, companies must first asses their own security strategy. Some organizations are using data lakes as a solution for managing data, but that can be a challenge for enterprises that keep data in data centers and the cloud, or in multiple cloud regions. Major data management vendors have been addressing this issue by using AWS, which offers elastic storage and CPUs.
As human beings “came online” and began generating data directly, enterprises had to adapt to the traffic patterns this created. We've been generating video content, work content, business transactions and new forms of content that are only now emerging. The infrastructures for data in transit and data at rest had to rapidly adapt, massive data centers for the cloud and asymmetrical network infrastructure had to emerge. It is the problem of managing this data and developing these information management strategies that has dominated data-driven enterprises.
The top 10 information management posts form CMSWire over the past year reflect these changing dynamics.
AI and machine learning (ML) became mainstream topics in the enterprise in 2017. Whether it’s a robot performing a backflip or self-driving Uber vehicles roaming Pittsburgh, organizations in many sectors have started looking for ways to use AI and ML to transform their operations. And this is just the beginning, IDC predicts that spending in this area will exceed $50 billion by 2021. We know AI and ML will change the way we work in the next several years, but amid all the hype, we take a deeper look at exactly what ML means for businesses everywhere today.
Kevin Burkhart, vice president of global consulting firm North Highland, once helped a client select a robotic process automation (RPA) application. It was perhaps the easiest business case ever to be made. The company had a set of over 1 million records, both digital files and scanned images, and it was seeking a way to quickly scan these files for information about when invoice payment was due. “The RPA system took 3 seconds to scan a test bank of 400 records,” Burkhart said. “A similar exercise using a group of people took four to five hours, and the RPA system was much more accurate.” In addition, he added, the RPA system uncovered additional information useful to the company, such as certain clients were charging late fees before an invoice came due.
There are four InfoSec trends that are key to understanding the industry in 2018. These are not emerging trends by any means. All of them have been important features of the InfoSec landscape for at least the last 18 to 24 months. They include:
- Security is top concern: Security ranks as the second highest priority for CIOs.
- Spending Boom: Information security spending will hit $93 billion globally in 2018, up 7 percent from 2017.
- Skills Gap: We have seen double digit growth in positions year-over-year, 200,000 open positions and zero unemployment.
- Information Risk: Data breaches are no longer anomalies, they are the cost of doing business.
Data is the fuel that powers many of the enterprise’s mission-critical engines, from business intelligence to predictive analytics, and data science to ML. To be fully useful, data, like any fuel, must be abundant, readily available and clean. The process of data ingestion — preparing data for analysis — usually includes steps called extract (taking the data from its current location), transform (cleansing and normalizing the data) and load (placing the data in a database where it can be analyzed).
Little (if anything) can stop the evolution and growth of the cloud business. With hybrid clouds shaping up to be the flavor of the year in 2018, we turn now to who the leaders in the cloud computing world are and will be for the foreseeable future. In spite of some jostling in the space, the leadership board for cloud services and Infrastructure-as-a-Service (IaaS) will likely remain unchanged: AWS leads, followed by Microsoft, followed by Google.
There is also some light on the horizon for IBM. Barclays bank upgraded the status of IBM’s shares on Jan. 16 and pushed their price target to $199 from $133 saying that “a new dawn emerges” for Big Blue.
Project management has evolved significantly in recent years, and there are plenty of project management tools to help facilitate those changes. But anybody following project management trends will know that technology is only half the discussion. Project management methodologies like Agile, Scrum and Kanban dominate the conversation. In this article, we explore these terms with the help of industry experts.
Red Hat users looking to maintain hybrid cloud or multi-cloud deployments because they can’t go “all in” on the cloud will benefit from IBM’s $34 billion acquisition of the enterprise open-source solutions provider, Nintex chief evangelist Ryan Duguid told CMSWire. Duguid and others offer thoughts on how the largest software acquisition to date will affect Red Hat users. In a conference call, IBM and Red Hat officials expressed confidence that the merger would not result in massive changes for customers.
In the world of technology, the mantra "innovate or die" is truer for organizations more now than ever, and AI is redefining industries by providing greater personalization to users, automating processes, and disrupting how we work. Like the adoption of cloud computing five years ago, the adoption of AI and the speed of its deployment varies according to industry. Here we look at some of the places where disruption from AI is already being felt. Alexey Sapozhnikov, co-founder and CTO of prooV, points out that while virtually every industry is embracing AI, it's the sectors that are stymied by well-worn processes and regulations — such as healthcare and government — that are likely to lag in AI adoption.
IBM recently pulled off the largest software acquisition in history by announcing its intention to acquire open-source cloud provider Red Hat for $34 billion. Experts say IBM’s acquisition of Red Hat opens a new dynamic in the cloud wars between IBM, Google, Amazon and Microsoft. They also say it gives IBM added muscles in the enterprise hybrid-cloud space, opens up a large conversation around containerized apps and microservices infrastructure, and begs the ultimate question: Will IBM’s massive bet on Red Hat save the struggling company or make it HP-Autonomy: Part 2, another massive deal gone south?
Enterprises are still wrestling with the problems IoT poses, rather than harvesting the benefits it offers. There is nothing unusual in this, given that IoT is still a relatively new phenomena and there are considerable — and justified — security fears dominating enterprise strategies around it. But will security dominate IoT this year? And what about Industrial IoT — will it move further away from IoT as some have suggested or will they become closer? At the beginning of 2016, we saw that the big tech vendors likes Microsoft were dedicating significant resources to building IoT incubators that would focus on enterprise, industry and smart cities.