How To Make Big Data Work For Your Business In 6 Steps

“If you use bad quality data to make decisions, the insights will be meaningless.” – Florence La Carbona: Enterprise Data Manager at TAL.

Good data is good for business. You can have a vast amount it, and the technical experts to analyse it, but that still doesn’t mean you’ll get the answers you need.

Here’s how to make Big Data work for your business in 6 steps.


#1 – DEFINE YOUR PURPOSE

“Big Data can be a lot like spring cleaning. You can come across a lot of ‘stuff’ you don’t really need, but you still have to dig into it. So where do you start?” – TechRepublic.

In a world saturated with ever-increasing data and information, it’s important to recognise what’s relevant and what isn’t. The best way to do this is to make sure the data you’re using is aligned with the use case at hand.

“Good data quality always depends on the context in which it is used.” – bi-survey.com

Picking the right use case involves clearly defining your business outcomes. What are you trying to achieve with Big Data Analytics?

“The Business outcome will help the organisation stay focused on finding the right match for the business challenge. From there, they can clean and link only the most pertinent data.”- Yoni Malchi: Author at World Wide Technology.

 

#2 – IMPLEMENT THE RIGHT TOOLS

“Are ‘data-rich’ organisations really leveraging their data to support continuous improvement? To succeed at this they must provide user-friendly tools that turn what is often an overwhelming amount of data into actionable insights.” – Menno Veeneklaas & Tibor Schwartz: Partners In Performance.

Gone are the days where businesses struggled extract meaning from mass volumes of raw data and wait several days, even weeks, for results. New software tools have taken the pain out of the process involved in collecting and analysing data. Products like Hadoop, Pig, Hive and Spark allow you to create your own Big Data stack and build your own solution platform.

“New architectural concepts such as data lakes or technologies like Spark and Hadoop require enterprises to rethink their data pipelines, starting at the source where data is produced, to how it is transported and eventually stored and prepared for analysis.” – Kumar Srivastava: Contributor at TechCrunch.

 

#3 – BECOME A DATA DETECTIVE

“The more familiar you are with the data, the easier it is to spot something that seems strange. A good place to start is by looking at the raw data to see what jumps out.” – Matthew Peters: Research Scientist at Allen Institute of Artificial Intelligence.

Although it may seem like a dull task, taking the time to make sure you’re collecting the best data possible will give you a significant advantage when it comes to increasing your profitability and achieving your business goals.

“Working to make sure that your organisation has the most accurate data on its clients possible can seem quite tedious. However, software tools from providers make the process of collecting accurate data simple.” – Experian.

 

#4- KEEP TRACK OF YOUR PERFORMANCE

“Each key performance indicator should be defined to measure the quality, enhancement over time and ways in which to improve a specific set of data.” – Forbes.

The main purpose for implementing a Big Data Analytics strategy in any organisation is to see an improvement in performance by turning insights into solutions that drive competitive advantage.
It’s important to measure the performance of your data analytics project by measuring it against set objectives from its inception through to completion.

“Finding business intelligence in Big Data depends on identifying strong key performance indicators that deliver high value to the business.” – Mary Shacklett: Contributor at TechRepublic.

 

#5 – UNIFY YOUR DATA

“Demand is growing for analytics tools that seamlessly connect to and combine a wide variety of cloud-hosted data sources. Such tools enable businesses to explore and visualise any type of data stored anywhere, helping them discover hidden opportunity in their IoT investment.” – Tableau.

Data can tell you what you need to know, but only if you can see it clearly. By building a single source, 360-degree view of integrated data, your team can access and drive value from a cohesive analytic environment.

“A unified data architecture is a more comprehensive view of the overall enterprise architecture; a collection of services, platforms, applications, and tools that make the best use of available technologies to unleash the optimal value of data.” – tdwi.org.

 

#6 – START WITH PEOPLE, NOT TECHNOLOGY

“How do you harness the power of software-defined solutions, and how do you get yourself ready for the next phase of your business’ IT strategy?” – Logicalis.

Implementing Data Analytics doesn’t start with technology, it starts with people.

“How can the CIO and his team introduce big data into their workflow, and how can they translate what appears to be hieroglyphics to top-level executives in plain language?” – Aberdeen Essentials.

It all starts with knowledge. This involves de-mystifying the common buzz words around Big Data and Analytics, so that your team can communicate effectively about what you are trying to achieve. The second step is to approach Data Analytics in a way that’s relevant to your team.

“Ask questions about the pain points that people feel in their everyday jobs. This presents data analysis as the solution you know it can be, rather than the burden someone else may see it as. Approach your intent to get your office on board with data analysis as a way to make the team even stronger, and a way to empower each individual to do his or her job better, and to make better informed decisions.” – Kelli Simpson: Former Marketing Manager at DataHero.

 

For more resources, please see below:

Data Quality Importance

Unified Data Architecture

Top Ten Big Data Trends For 2017

From Big Data To Real-Time KPIs

Setting a KPI Course For Big Data

Big Data Project: Objectives First, Plan Second

How To Maintain A High-Quality Big Data Company

Nine Tips To Improve Data Quality & Improve Decisions

The Importance of Data Quality: Good, Bad, or Ugly

Getting Your Organisation To Embrace Big Data Analytics

Merging Key Performance Indicators With Big Data Analytics

For Analytics To Be The Answer, You Need The Right Use Cases

How To Measure The Success of Your Big Data & Analytics Strategy

Data Quality & Master Data Management: How To Improve Your Data Quality

“Big Data”, Business Intelligence (BI) and Key Performance Indicators (KPIs)

Five Tips For Data Efficiency

At Contexti, we’re always looking for new ways to make it easier to work with data.

When it comes to Big Data projects, it’s all about efficiency. We’ve rounded up the five best tips on how to make it happen.

 

#1 – DATA COMPRESSION

This can be a great way to reduce repetitive information, have shorter transition times and free up some storage space. The process of encoding data more efficiently to achieve a reduction in file size can happen in two ways: lossless and lossy compression.

“Lossless compression algorithms use statistic modeling techniques to reduce repetitive information in a file. Some of the methods may include removal of spacing characters, representing a string of repeated characters with a single character or replacing recurring characters with smaller bit sequences.” – Conrad Chung: Customer Service & Support Specialist at 2BrightSparks.

The great thing about lossless compression is that no data is lost during the compression process. With lossy compression, data such as multimedia files for images and music can be discarded. Lossy compression on the other hand, works very differently.

“These programs simply eliminate ‘unnecessary’ bits of information, tailoring the file so that it is smaller. This type of compression is used a lot for reducing the file size of bitmap pictures, which tend to be fairly bulky.” – Tom Harris: Contributing writer at HowStuffWorks.

 

#2 – CLOUD OPTIMISATION

“If your organisation wants to extract the highest level of application performance out of the computing platforms that it purchases, you should ensure that workloads are optimised for the hardware they run on.”- Joe Clabby: Contributor at TechTarget.

Choosing the right cloud services to achieve this requires consideration of efficiency, performance and cost advantage. A great tool for workload optimisation is the Cloudera Navigator Optimizer for Hadoop-based platforms.

“Cloudera Navigator Optimizer gives you the insights and risk-assessments you need to build out a comprehensive strategy for Hadoop success.” – Cloudera Inc.

Not only does it reduce risk and provide usage visibility, it’s also flexible and keeps up with changes in demand. “Simply upload your existing SQL workloads to get started, and Navigator Optimizer will identify relative risks and development costs for offloading these to Hadoop based on compatibility and complexity.”

 

#3 – UNIFIED STORAGE ARCHITECTURE

Many enterprises experience the same dilemma: unified storage system or traditional file/block storage system?

Randy Kerns, Senior Strategist & Analyst at Evaluator Group describes unified storage as “ A system that can do both block and file in the same system. It will meet the demands for applications that require block access, plus all of the file-based applications and typical user home directories you have.”

With the ability to simplify deployment and manage systems from multiple vendors, unified storage architecture is growing in popularity among storage administrators who are quickly seeing the benefits of the distributed access and centralised control it provides.

An article in TechTarget highlights the key benefits of running and managing files and applications from a single device: “One advantage of unified storage is reduced hardware requirements. Unified storage systems generally cost the same and enjoy the same level of reliability as dedicated file or block storage systems. Users can also benefit from advanced features such as storage snapshots and replication.”

 

#4 – DEDUPLICATION

“Deduplication is touted as one of the best ways to manage today’s explosive data growth.” – Brien Posey: Technology Author at TechRepublic.

Data deduplication is a technique of eliminating redundant or duplicate data in a data set and as a result, maximising storage savings and increasing the speed and efficiency at which data is processed.
By reducing the amount of storage space an organization needs to save its data, you’re not only saving time and money, but you’re preserving the integrity and security of of your data. “The simple truth is that to be effectively managed, adequately protected and completely recovered, your data size must be shrunk.” – Christophe Bertrand: VP of Product Marketing at Arcserve.

Here’s how it works: “Each chunk of data (e.g., a file, block or bits) is processed using a hash algorithm, generating a unique number for each piece. The resulting hash number is then compared to an index of other existing hash numbers. If that hash number is already in the index, the data does not need to be stored again. Otherwise, the new hash number is added to the index and the new data is stored.” – TechTarget.

 

#5 – CROSS-CHANNEL ANALYTICS

“Cross-channel analytics is a where multiple sets of data from different channels are linked together and analyzed in order to provide customer and marketing intelligence that the business can use. This can provide insights into which paths the customer takes to conversion or to actually buy the product or avail of the service. This then allows for proper and informed decision making to be made.” – Techopedia.

Among the many benefits of this process are understanding the impact of each channel, how they work together and determining which channel combinations get the highest results and conversions. It’s an efficient system that generates insights useful to each department within your organisation.

“Business leaders can use this information to design better process flows for customers by creating or revising customer journey maps. Meanwhile, marketers can use behavioral data from customer interactions in different channels for other purposes.” – TIBCO Blog.

 

For more resources, please see below:

 

Data Efficiency

What Are The Data Efficiency Technologies? – Performance: The Key To Data Efficiency

 

Data Compression

How File Compression Works

How Big Is Your Data, Really?

The Basic Principles of Data Compression

Data Compression: Advantages and Disadvantages

 

Cloud Optimisation

Cloudera Navigator Optimiser

Application Performance Tips: Workload Optimisation and Software Pathing

 

Unified Storage Architecture

Advantages of Using Unified Storage!

Unified Storage (Multiprotocol Storage)

Unified Storage Architecture Explained

Unified Storage Architecture: The Path To Reducing Long-Term Infrastructure Costs

 

Data Deduplication

What Is Data Deduplication?

How Data Deduplication Works

10 Things You Should Know About Data Deduplication

The ABCs Of Data Deduplication: Demystifying The Different Methods

Understanding Data Deduplication – And Why It’s Critical For Moving Data To The Cloud

 

Cross-Channel Analytics

What Is Cross-Channel Analytics?

Big Data Analytics: The Key To Understanding The Cross-Channel Customer

Contexti’s Big Data as-a-Service In The Cloud Just Got Better With Cloudera Altus!

We’re excited by the recent announcement of our partner Cloudera on the availability of Altus, which takes the deployment of data platforms and data pipelines in the cloud to the next level.

“Leveraging AWS cloud and Cloudera Enterprise, Contexti has a track record of providing big data-as-a-service / big data platform services for Australian customers including for Seven West Media’s coverage of the Rio Olympic games.” said Sidney Minassian, Founder & CEO of Contexti. “With the availability of Cloudera Altus we’re looking forward to enhancing our service offering for customers who are leveraging their data for value creation.”

Seven West Media taps Cloudera and Contexti for Big Data Solution for Rio Olympics

Cloudera Altus features include:

  • Managed service for elastic data pipelines
  • Workload orientation
  • Backward compatibility and platform portability
  • Built-in workload management and analytics
  • Faster cluster provisioning times
  • Integrated security with cloud service provider solutions

To learn more about Altus, read Cloudera’s blog: Simplifying Big Data in the Cloud