Six Top Data Management Practices Every Organization Must Follow

Six Top Data Management Practices Every Organization Must Follow

Storage silos in most traditional organizations are bursting open from the rapid evolution in big data. Most of these organizations are now concerned about data management practices in their organizations.

In the last decade or so, every industry from manufacturing to advertising has migrated to multichannel sourcing of data. This means each individual set of data now competes with every other set for analytical significance. Businesses can easily stretch out of their means of trying to fuel this process. Resultantly, very few companies can claim that they are making the best use of their data.

By all means, the answer lies in implementing a data management solution that is practical. Plus, it should improve the quality of the collected data. Moreover, it can also be a vital step toward solving productivity issues.

The focus is steadily shifting toward the production of well-analyzed, relevant and timely data. Such data allows businesses to make improved decisions and usher in substantial growth. Fitting in data management solutions in business could be challenging. And if you have not started yet, you might totally miss out on what’s actually covered in data management.

With a data management plan that is centered on specific business needs, every new asset in data will undergo extensive monitoring processes to make sure there are no security threats and data is kept safe. Here are some top data management principles and practices that will help your organization make the most of the available data assets.

Understand your business goals before data objectives

Over the next decade or so, the volume of data will snowball into a living data giant. In parts, this development will be propelled by the new digital devices that are constantly being added to systems and networks. The uninterrupted flow slows down data collected previously further down the silos as newer sets of data assume more importance.

Using data to understand and realize business goals is quite common as a practice. But a data would scientist would recommend that organizations keep referring to the business goals throughout the process of data planning. This helps companies identify the most important data sets and understand whether or not those need to be placed in a silo.

As an organization, you also need to consider how every dataset can impact the KPI that you would want to improve. Based on the goal you set, you will have to make a decision on what data you want to store. At the moment, most organizations do shoddy data management.  They store a lot of data without a well-defined purpose or store mechanism.

The best way to work around this is to know and decide how much data and associated technologies you will need to crack the goal.

Club AI and machine learning in data management

The more datasets an organization accrues, the more time it takes to conduct analysis and reporting on every one of them. With new techniques like artificial intelligence, the extraction levels on the collected datasets are all set to go deeper with machines getting contributing a bigger chunk in the analysis. Data companies are already championing inter-technology collaboration to better facilitate GDPR guidelines.

The other big factor in data management is big data. Given how big data has become in the past few years, artificial intelligence will be an even bigger factor in the months and years to follow. AI can deliver fast, economical and high-quality intelligence from ginormous sets of data. It is beyond impossible for humans to derive actionable insights from such data volumes.

With the onset of GDPR, almost any organization that dealing in significantly large volumes of data will need artificial intelligence. The major ways in which AI will help companies in better data keeping include:

  • The ability for consumers to check in and out of official communications
  • Supply consumers with reports on what data the company collects from them
  • Give consumers easy ways to delete all data the company has about them

Without artificial intelligence supplying the necessary technology, these processes will become heavily time-consuming for businesses.

Ensure the right people manage data

A good data strategy for a business starts with placing the best practices and principles in place. However, what you want to know is that success is a result of the right people managing data for your organization.

Start with planned data governance. Deriving maximum value from data is critical to any data strategy of a business. Perhaps, the first of many steps in data strategy is to include data governance as a principle. For one, this will make sure that the data being used in the business continues to stay of the highest quality throughout its lifecycle.

Data governance is a process in the evolution of new businesses. Since it’s based on integrity, usability, and availability, it allows for the whole industry to make use of the data. With big data and analytics, companies can improve security, reduce costs, ensure compliance, improve data quality and derive meaningful insight.

Implementing an enterprise-wide governance framework to reduce the cost of operation and risk associated in the subsequent projects.

Make data accessible

Data security is as important for an SME as it is for a Fortune 500 company. But in a mad bid to secure data, companies cannot afford to forget data, which might, in the long run, make it defunct altogether. Data needs to be stored securely, but without compromising on the accessibility for those who need the data. Imperatively, the same data should not be available to those who do not have the proper clearance.

Staying on the top of data access protocols is key to cope up with the rapid leaps into the digital age. Organizations must make sure that data is stored at places where relevant groups can have easy access to them. The age that is coming is more data-driven than we would think. At that, it is relevant that organizations are adequately prepared to extract data from dashboards. The message here is simple – silo data is not of any particular use to a company.

Defend cybersecurity threats

Most companies have an Incident Response Plan by now. But the common mistake that most companies end up making is to deviate from that plan. So first of all, there has to be a clear plan accentuated with decision points in times of crisis. That will let companies know if there is a legal requirement, good faith or regulation to find a breach which is either potential or realized.

To start with, an Incident Response Plan should be established before the occurrence of any major incident. The plan should include all the points that will help in recovery, eradication, containment and also supply with expert testimony.

Democratize data management 

Data management principles and practices must be kept up collectively by a business. Using a holistic method to work lets every member in the company to gain access to data infrastructure and create a way for better data management processes. Along with solid governance, this method can introduce successful master management of data as well. But for company-wide success, the integration must first happen within the company.

Data management practice aids in the study of data in the correct perspective to arrive at conclusions that align with business objectives. Now that organizations are hoarding lots and lots of data, the key is in classifying the data well and making a senior official in the company accountable for it.

Data democratization is desirable. There’s no question about that. However, it has surpassed desirability. With GDPR rolling into action faster than most would have imagined, someone within an organization has to take responsibility for the data of their users. Moreover, implementing stricter data guidelines will also ensure that companies are aware of the kind of data that flows through their organization.

If you follow these recommended data practices, you will be that much closer to making holistic use of data.

Futran Solutions specializes in delivering composite data management and analytics for small and medium enterprises. As applications of data management in business keep evolving, so do the resources that shoulder these needs within an organization. Speak to a Futran Data Analytics specialist today. Find out how we help you achieve your business and marketing objectives.

Seven Hottest Analytics And Big Data Trends For 2019

The Big data is the vast volumes of data generated from a number of industry domains. Big data generally comprises data collection, data analysis and data implementation processes. Through the years, there’s been a change in the big data analytics trends – businesses have swapped the tedious departmental approach with data approach. This has seen greater use of agile technologies along with heightened demand for advanced analytics. Staying ahead of the competition now requires businesses to deploy advanced data-driven analytics.

When it first came into the picture, big data was essentially deployed by bigger companies that could afford the technology when it was expensive. At present, the scope of big data has changed to the extent that enterprises both small and large rely on big data for intelligent analytics and business insights. This has resulted in the evolution of big data sciences at a really fast pace. The most pertinent example of this growth is the cloud which has let even small businesses take advantage of the latest technology.

The modern business is floating on a stream of never-ending information. However, most businesses face the challenge of extracting actionable insights from vast pools of unstructured data. Despite these roadblocks, businesses are deriving from the tremendous opportunities for growth presented by big data. Here is all that would count as the hottest big data analytics trends of 2019.

Booming IoT Networks

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Like it’s been through 2018, Internet of Things (IoT) will continue to trend through 2019, with annual revenues reaching way beyond $300 billion by 2020. The latest research reports indicate that the IoT market will grow at a 28.5% CAGR. Organizations will depend on more structured data points to gather information and gain sharper business insights.

Quantum Computing

 

Industry insiders believe that the future of tech belongs to the company that builds the first quantum computer. No surprise that every tech giant including Microsoft, Intel, Google and IBM are racing for the top spot in quantum computing. So, what’s the big draw with quantum computing? It allows seamless encryption of data, weather prediction, solutions to long-standing medical problems and then some more. Quantum computing allows real conversations between customers and organizations. There’s also the promise of revamped financial modeling that helps organizations develop quantum computing components along with applications and algorithms.

Analytics based on Superior Predictive Capacity

 

More and more organizations are using predictive analysis to offer better and more customized insights. This, in turn, generates new responses from customers and promotes cross-selling opportunities. Predictive analysis helps technology seamlessly integrate into variegated domains like healthcare, finance, aerospace, hospitality, retailing, manufacturing and pharmaceuticals.

Edge Computing

 

The concept of edge computing among other big data trends did not just evolve yesterday. Network performance streaming makes use of edge computing pretty regularly even today. To save data on the local server close to the data source, we depend on the network bandwidth. That’s made possible with edge computing. Edge computing stores data nearer to the end users and farther from the silo setup with the processing happening either in the device or in the data center. Naturally, the entire procedure will see an organic growth in 2019.

Unstructured or Dark Data

 

Dark data refers to any data that is essentially not a part of business analysis. These packets of data come from a multitude of digital network operations which are not used to gather insights or make decisions. Since data and analytics are increasingly becoming larger parts of the daily aspects of our organizations, there’s something that we all must understand. Losing an opportunity to study unexplored data is a big-time potential security risk.

More Chief Data Officers

 

The latest trendy job role on the market is that of a Chief Data Officer. Top-tier human resource professionals are looking for competent industry professionals to fill this spot. While the demand is quite high, the concept and value of a CDO are largely still undefined. Ideally, organizations are preferring professional with knowledge in data analysis, data cleaning, intelligent insights and visualization.

Another Big Year for Open Sourcing

 

Individual micro-niche developers will invariably step up their game in 2019. That means we will see more and more software tools and free data become available on the cloud. This will hugely benefit small organizations and startups in 2019. More languages and platforms like the GNU project, R, will hog the tech limelight in the year to come. The open source wave will definitely help small organizations cut down on expensive custom development.

Making of a Storm: What Happens to Dark Data in Analytics and Big Data?

Making of a Storm: What Happens to Dark Data in Analytics and Big Data?

Dark data is the kind of data that does not become a part of the decision making for organizations. This is generally the data from logs and sensors and other kinds of transactional records which are available but generally ignored. The largest portion of the yearly big data collected by organizations is also dark data.    

Dark data does not usually play a vital role in analytics because:

  1. Companies do not want to use their bandwidth on additional data processing
  2. There’s a lack of technical resources
  3. Organizations do not believe dark data adds any value to their analytics

All of these are valid reasons for the data taking the back seat. But today we have a string of data-centric technological advances. Together, they present a heightened ability to ingest, source, analyze, and store large volumes of data. With that, it becomes important for organizations to recognize this largely untapped volume of data.   

The conventional way to use this data would be to systematically drain all of it into a waterhouse of data. This is followed by the identification, reconciliation, and rationalization of the data. The reporting follows soon after. While the process is pretty methodical, there might not be as many projects that truly call for such a need.   

The Immense Volume of Dark Data in Enterprise

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At the moment, we have solid  evidence to suggest that as much as 90% of all data used in enterprises could be dark. Since industries are now storing large data volumes in the ‘lake’, it should be natural to tag the data appropriately as it gets stored. Perhaps the key is to extract the metadata out of this data and then storing it. 

Profiling and exploring the data can be done using one or a combination of tools that are already available in the market. Cognitive computing and machine learning can further increase processing power and open up possibilities of making intelligent use of dark data.  

Dark data may or may not have an identifiable structure. For example, most contacts and reports in organizations are structured. But over the course of time, they add up to the pile of dark data. Unstructured data can be small bits of personally identifiable info like birth dates and billing details. In the very recent past, this type of data would remain dark.

Machine learning can help organize this data in an automated manner. It can then be connected to other attributes of data to generate the complete view of the data. Using geolocation data is slightly trickier though. While it is extremely valuable, the lifespan is rather short. A collection of historical geolocation data sets can be further leveraged using machine learning to aid in predictive analysis of data.    

Recognition of regular data as dark data

Other sets of data often considered “dark” in the past include data from sensors, logs, emails, and even voice transcripts. The longest stretch they would get in terms of application would be vested in troubleshooting purposes. Not many would look to make such data a part of actual decision making. Now that we can convert voice or text (and vice versa) and use the data to gather intelligence, there are many use cases that draw advantage of data traditionally considered dark.    

An IDC estimate suggests that the total volume of data could be somewhere close to 44ZB (zettabytes) in 2020. This data explosion will be influenced by many new data generators like the Internet of Things. And unless we light up this data with new technology and processes, a large volume of it will continue to stay dark.  

The first and obvious step will be to make all the dark data available for exploration. The second step is to categorize the data, scrape out the metadata and do a quality check for all the extracted data. Modern tools for data management and data visualization provide the ability to explore the data visually. This determines whether or not the data can be illuminated to remove the visual noise.      

The myriad advances in Artificial Intelligence (AI) will definitely aid in uncovering the secrets of the oft-ignored “dark data”. However, the trick is still in using the data prudently. Wrong use of data will inadvertently result in incorrect predictions and may invite regulatory sanctions.

The vastness of dark data demands handling by Big Data and AI experts. In addition, there needs to be a clear plan about the application of the data once it is sorted. At Futran Solutions, we work with a pool of incredibly talented Big Data and Artificial Intelligence experts who can help your organization make the most of dark data. Contact us today to talk solutions in big data and artificial intelligence.