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What is the best way to arrange large sets of data?

It’s better to take a proactive approach to sorting out, storing and handling large data from the onset.
By designing a cohesive data strategy, and developing and adhering to data organisation procedures.

 

 

Most modern enterprises have more data than they know what to do with the ‘treasure trove’. Data is the ‘new gold’. Yet very few are arranging and using their data in a way that really benefits the business. Here we talk you through some of the top strategies for arranging large data sets.

 

What do we mean by large sets of data?

Many standard software systems and asset management system are built for coping with large collections of figures, files and assets.

These systems generally do pretty well at handling data sets that have a couple of thousand, or even tens of thousands of records.

Large data sets are those that the most common data processing systems struggle to deal with. While there isn’t a specific figure that correlates to a large data set, you can consider it to be large when handling the data is pushing your current data solutions to the edge, making data difficult to deal with.

 

When large data goes bad

When you have large data sets, they are often complex. They may be lacking consistent structure and be unwieldy and difficult to use.

Trying to process a large data set in a generic program will likely result in a need for the data to be broken down and dealt with separately- which can add time and costs to your operations.

Large data sets not stored appropriately can cause programs to fail and systems to crash and make it impossible for any analytics to be carried out.

 

Resolving large data issues

If large data sets do cause problems for your systems and software, there is generally a need to make some changes in how you’re handling the data.

You might need to invest in a more sophisticated data management software system, and have a think about how you are storing, taking care of and using data.

When such fails occur, it is wise to conduct a proper degree of analysis to assess the conditions which caused a computer to fail, as well as a reconsideration of how data is manipulated within the system. You might even need an assessment across your overall IT infrastructure and approach.

 

Why bother with data

Data records are created across so many systems in so many ways. If you’re in an enterprise business, you will know how important data is becoming in day-to-day operations.

Data analytics systems give you the feedback you need to respond to trends and pressures and gain insight into customer behaviour and activity. Using data well can give you an edge over your competitors.

But if you’re going to deal with data effectively, you need both commitment and discipline.

 

Select the right software

There are lots of specialist software solutions out there that can help you manage, arrange and investigate your data.

When your data collection is considered large, there is no way you can manage it using the basic administration systems we are familiar with.

The right software will enable you to use your information in ways that are both useful and meaningful, and without the need for manual input and analysis.

 

The ethics of large data

As the data revolution continues, we are all being encouraged to think about how we can use data to acquire new customers and more sales, and yet we also need to be thinking about the ethics of this data acquisition.

Privacy laws are changing to help ensure that businesses use data in an ethical way and within the legal framework. If you are storing large data sets, you’ll need to make sure that you are doing so in a way that meets consumer law and contemporary social standards.

 

How to organise large sets of data for research

People play an important role in data management. Invest time and resources not only into the systems but the people who need to use them.

There isn’t a system out there that can operate and serve your needs without some degree of human involvement. In a large organisation, it’s important that data is treated as an essential business asset, and that your people are aware of and are interacting with data in an effective way.

Ensuring your whole teams are familiar with data infrastructure and protocol is part of the running of an enterprise business. Even if they aren’t dealing with data frequently, everyone from sales reps to stock controllers need to familiar with local systems and practices.

 

 

Assign responsibility

When you’re running a big operation, and your data is coming in from multiple systems, it is worthwhile clearly allocating specific people with specific sets to manage and organise.

When individuals are clear about their responsibilities and are regularly checking the data for integrity, your whole data system and network will perform better.

 

Know the value of your data

Data and systems people have long known it to be the case, but leaders and executives are now recognising the importance of data as a driver for growth and expansion.

Dealing with and learning from the large data held by organisations is now seen as core business. One report found that businesses that employ chief data officers to help businesses gain leverage from their extensive data can improve business value by a factor of more than x2.5.

 

Three-step data handling

The top three stages for creating a strong data strategy are:

  1. Appropriate data collection- this means being disciplined and consistent in the ways data is collected and amassed. In turn, this means clear data acquisition processes and n a commitment to data integrity. Missing, duplicated or incorrect data needs to be dealt with accordingly.
  2. Creating proper data rules- which means ensuring you have effective and appropriate guidelines and rules which systems can easily interpret and which provides ease of use to the people involved in the processes
  3. Using the data wisely- the entire goal of having large data sets is to use the data wisely and to competitive advantage. Data modeling methods can help clarify what this will look like for you.

 

How to analyse big sets of data

There are numerous data analysis tools and techniques out there.

  • Data mining- the identification and extraction of information that reveals patterns and trends. For example, assessing which market segments would be more likely to respond in a particular way to an offer or campaign
  • A/B testing- changing variables within one situation or control group to determine how altering certain factors might lead to a different outcome. For example, assessing how changes to a website might lead to increased conversion rates.
  • Data integration- using techniques to combine and integrate data rather than looking only at a single source.
  • Statistical analysis- a more traditional method of the interpretation of data which has resulted from a survey or experiment.
  • Machine learning- the use of algorithms from within the large data to make predictions.

Other ways you might analyse big sets of data include:

  • association rule learning
  • network analysis
  • Natural language processing (NLP).
  • spatial analysis
  • predictive modeling

 

How to best store large sets of data

It is worthwhile doing a data risk assessment to help you be really clear about what risks you may encounter when dealing with data storage. It is worth assessing:

  • How much storage is required now, and into the future?
  • How can data be accessed by those who need to have access?
  • Is cloud storage the best option?
  • Which safety and security requirements does this data need to be handled with?
  • What data backup operations are possible?

 

Consider scale

Always be prepared for the acquisition of more data. Whatever data you have now, you can expect your overall requirements to increase in the future.

When you are working with bigger and more complex sets of data, you need a system that can rise to the challenge. Don’t just go with something that will get you by in the short term. Future-proofing your system and strategy is critical.

 

Consider flexibility

While you want to set out good patterns and procedure for your data sets, you will also need to consider how flexible your data system might be.

Sometimes, external systems change, public expectations shift, or you need to do things differently to better serve your customers.

Being flexible within a data strategy means that you operate within a change mindset. Selecting a data management tool that has inherent customisation will help you to be able to adapt to changes without losing work or performance.

 

Files and folders

Proper organisation of data the file and folder level will make it much easier for you to locate, access, and retrieve information when you need it.

Many systems pull metadata and fields from base or raw files, which are stored locally or in the cloud. The best time to think about data control is at the start of a new project or when you embark on the use of a new system. Establishing proper file and folder conventions from the onset allows you to:

  • sort and group files across data fields and sets
  • apply rules to data sets
  • create patterns and rules which will keep data cohesive

 

Tips for the creation of data records

  • Apply a hierarchy whereby there are fewer folders at the upper levels and more as you progress through lower levels
  • Keep file names short and clear, but relevant and meaningful
  • Use a standardised vocabulary
  • Specify how numbers will be used and the amount of digits required in any file figure
  • Create a file that accompanies data sets so that other users can read and understand the naming protocols
  • Use dates in a consistent format, and consider using the year at the start so that files can viewed both alphabetically and in chronological order
  • Avoid using spaces and special characters as some operating system cannot interpret them

 

How much data is out there?

Do you really want to know just how much data we create around the globe each day?

The figures are staggering. According to IBM, about 2.5 quintillion bytes of data are created every day- enough to fill up 57 billion 32 GB iPads.

We send over 290 billion emails and do more than 5 billion searches. There are five hundred million tweets on Twitter and sixty-five billion WhatsApp messages.

And the amount of data that we produce is accelerating rapidly. It has been estimated that there are 40 times more bytes of data than there are stars in our known universe.

 

Big data for the big players

However much data your dealing with, it’s unlikely to be as much as some of the big internet companies out there. In early 2020, Google published 25 million free datasets.

And while they don’t curate or provide direct access to the data sets, they publish using metadata applied by the providers.

According to Google, most of the datasets are related to geosciences, biology, and agriculture.

 

The future of large data

The recent Gartner report about data trends for 2021 found that we may actually be coming full circle on large data.

In some cases, enterprise organisations are seeking to make data sets smaller, more agile, and easier to deal with because they are held in smaller sets.

Smaller data sets require fewer records, but can be used in a flexible way to offer useful insights at a simpler level. In order to improve data composability, the key here is to ensure that data held across different systems can be effectively integrated and that each unique set can be easily related to the others.

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