7 Simple Steps to Prepare for Big Data

Big data is definitely popular. In fact, more organizations are investing in their own data platforms to really gain as much value as they can out of the information they’re creating.

Gartner recently pointed out in a survey that investment is increasing, but organizations are still struggling with understanding how to leverage and even prepare for the world big data. “Investment in big data is up, but the survey is showing signs of slowing growth with fewer companies having a future intent to invest," said Nick Heudecker, research director at Gartner.

"The big issue is not so much big data itself, but rather how it is used. While organizations have understood that big data is not just about a specific technology, they need to avoid thinking about big data as a separate effort."

So, to simplify it all just a bit, let’s look at these 7 simple steps to prepare for big data:

1. Understand your data sources & the types of data you’re creating

Are you leveraging structured or unstructured data? Also, what are your data sources? Are they coming from CSV files or maybe a web-based XML tool?

It’s critical to classify and understand your data to really be able to leverage data analytics and data visualization tools. However, there are also solutions out there that integrate machine learning to auto-classify your data sources for you.

For example, advanced data warehousing solutions use common transformations automatically, including the identification of data formats like CSV, TSV, JSON, XML, and many log formats.

2. Make sure you have an infrastructure that can support big data

The impact that big data has on enterprise networks and IT infrastructures is multidimensional and driven by the three Vs: volume (growing amounts of data), velocity (increasing speed in storing and reading data), and variability (growing number of data types and sources).

This is why storing big data and doing all of the processing onsite doesn’t always make sense. In some cases, working with a cloud-driven data warehousing solution might make a lot of sense.

3. Understand how to store and process big data

You’re not just trying to create a simple report. Rather, you want to create powerful data visualization and data analytics capabilities. This is why working with data warehousing can make big data much easier to leverage.

Data warehousing allows you to leverage a central repository of information that is used for reporting, data analytics, and can become an integral part of your business. Furthermore, you introduce greater levels of agility when data warehousing is done in the cloud!

4. Data analytics is only a part of the entire solution

As a part of data warehousing, you can leverage a variety of other services and solutions. For example, if your business logic is complex, you may want to layer on a transformation tool that bakes it into your data before it hits your BI tool. 

Likewise, if you want to "close the loop" on data, you may want to feed the results of your analysis back into operational tools like Salesforce. That can help your business to make better decisions more quickly by keeping key insights front and center.

5. Leverage cloud

As I mentioned earlier, on-premise solutions will have their limitations. This is where, if you really want to be competitive in the market, you start to look at cloud data warehousing. These types of solutions not only integrate with on premise architecture, they help scale data across major cloud vendors like Azure, AWS, and Google.

6. Align your business to turn petabytes into profits

In the digital age, organizations need a better approach to harness unstructured and multi-structured data and to combine it with ever-growing volumes of big transaction data. When you align your business strategy with IT, you create a clear vision into how core data sets are going to be used to make better business decisions.

In fact, with a good approach, data analytics, data visualization, and even data warehousing all help organizations improve operational efficiency, combat fraud and customer churn, enhance products and services, and increase insights and profitability.

7. Build (or borrow) a team to help 

You absolutely don’t have to go on this journey alone. In fact, I really don’t recommend that you do. Data scientists are an entirely different breed of technology professionals. A data science team can actually act as the catalyst for businesses getting true value from big data. These teams can help you create a sustainable data model, leverage data visualization, and even help you streamline the data journey from source to analysis.

"When it comes to big data, many organisations are still finding themselves at the crafting stage," said Jim Hare, research director at Gartner. "Industrialization—and the performance and stability guarantees that come with it—have yet to penetrate big data thinking."

So, this is your chance to get ahead of the curve. Those organizations which realize the value of data and know how to integrate it are the ones who create real-world competitive advantages. A major challenge with big data today is the limited approach that some organizations take. A big recommendation is to work with a good partner and data scientist team to help you understand your own data sources and how to best leverage big data. From there, you can align your business, impact your processes, and really utilize the power of big data.

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