Data Strategy Head to Head: Which solutions are best for your business?

In all my years of leading and managing data management projects, there’s one telltale sign a business needs to rethink their current setup: when adding a new data source is overly time-consuming and clunky.

Organisations have vast data sets spread across multiple systems. To be able to work with that data effectively, they need to bring it together into one usable resource. The question is how to do that without actually moving the data. 

Unfortunately, you can’t know which data management solution is right for you just from a blog, but a technology partner like 101 Ways can work with you to find the best solution for your business. For now, I’ll put three popular data management solutions head-to-head and look at some of the pros and cons they could bring to your business, so you can take the first step towards better data management.

Suggested reading: Download our eBook on how to become a data-driven organisation, and learn more about how better data management can drive growth.

Data Warehousing: The Structured Silos of Data

I’ve seen data warehouses become more and more common, as organisations developed a ‘gather now, find uses later’ policy for their data. Lots of data is never a bad thing, but understanding how best to manage that data and extract value from it is where a lot of leaders fall short. 

Pros of Data Warehousing 

The main benefit of data warehousing is that all your data is sorted into a schema as soon as it flows in, which makes data relatively simple to retrieve. 

Data warehouses ensure that your data is reliable and of a high quality. They’re also more cost effective when it comes to analysing data, instead of separately implementing a data quality management programme.

How these warehouses store big data makes big data more consistent, no matter how diverse the source. This way, you can be confident the decisions you’re making are based on strong, accurate data.

Cons of Data Warehousing

Considering data warehouses are managed by data teams, they’re often a source of bottlenecks due to having to go through these middlemen to access information.

Additionally, data warehouses tend to have very specific schemas and governance that were written at the time of creation, which are hard to adapt to changing needs, data sources and structures. Changing data in your data warehouse has significant knock-on effects, which can be risky and requires a lot of planning and development. 

Since data warehouses are so expensive and take a long time to get right, they are often required to first serve C-Suite operational and reporting needs. This means it takes even longer before individual product teams start getting valuable insight. 

Data Lakes: A Raw Repository of Data

Data lakes have grown in popularity since their inception in 2015 as organisations want access to data faster.1 These vast pools of data in its native or ‘natural’ format can speed up getting insights. At the same time, data lakes require a varied set of skills to oversee properly — a set of skills that’s extremely hard to come by. 

Pros of Data Lakes

Data lakes were developed to overcome some of the limitations of data warehouses. Because the data doesn’t have to be structured in order to be stored, data scientists can access it quickly and easily.

Data lakes can combine data from a range of sources (CRM platforms, marketing platforms, social media) to give you a clear picture of who you’re catering to. This helps you tailor how you work and the way you lead your team, as you’re basing it off of detailed data. 

Data lakes also accommodate larger data sets, a really valuable feature for large corporations or government organisations.

Cons of Data Lakes

The biggest drawback of a data lake is that raw data is stored with little to no oversight of what it contains. When this data isn’t catalogued, it gets lost or marked as untrustworthy, causing a ‘data swamp’.

Much like data warehousing, data lakes are held back by middlemen. Rather than letting data producers and data consumers federalise the data management process together, you’re stuck going through the data team, once again creating bottlenecks.

Pro tip: Data platforms are integral to your success when you want to become a data-driven organisation. Learn more about them in our blog.

Data Mesh: Decentralised Domain Ownership of Data

I’ve watched data mesh architectures boom in popularity, as they’re seen as a fresh alternative to centralised data management. Although, decentralising data can lead to its own share of problems when the people in charge of localised domains have no idea where to begin.

Pros of Data Mesh

By being decentralised, a data mesh removes the bottleneck that comes with having a centralised data team. Instead of having data gatekeepers, each domain has control over its own data. This streamlines and makes analyses simpler. 

Data mesh as a solution also means that not everyone has to be a data expert in order to access and utilise the data they need.

In data meshes, data is treated as a product rather than just a commodity, allowing each domain to provide high-quality data that meets the needs of consumers beyond the domain itself. Data is also being gathered with a specific purpose in mind, so you’re not saddled with tons of useless information that was collected aimlessly.

Data meshes have a self-serve infrastructure platform that is domain-agnostic. This provides a consistent platform for tools and functionality without slipping back into the old, centralised way of doing things. 

Despite this design, data meshes follow a federated governance model. Governance rules are set centrally, ensuring that each domain sticks to certain organisational standards and regulations.

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Cons of Data Mesh

Unlike data warehouses and lakes, data meshes rely on effective communication between domains. When there’s a lack of communication, data can be replicated (or contradictory), wasting time and resources.

Data meshes are designed to offer teams rapid, domain-specific insights. They aren’t designed to solve the same problems as data warehousing or lakes, but they can be used to build warehouses and lakes with the right governance. 

In decentralising your data, governance can become more difficult. The centralised data teams in warehousing and lakes, while occasionally bottlenecks, hold a wealth of knowledge about data that keeps it secure and compliant with all regulations.

Manage your data the right way

Determining the best data management practice for your organisation isn’t easy, especially since it can be difficult to look inwards and determine exactly where you’re being held back.

I also get that businesses may be reluctant about making fundamental changes to their data management setup. That is understandable, given that many businesses simply don’t have the expertise in-house to carry out such a change with confidence. 

This is why it’s best to work with a technology partner that has the know-how to ensure that your data management solution is actually supporting your organisation, rather than holding it back. 

With our industry-leading expertise, we at 101 Ways aim to enrich people’s lives through technology and digital solutions. We help our customers solve challenging problems and take the uncertainty out of technological change. 

To see how we could help you realise your data goals, get in touch with us today and talk to one of our experts. 

Footnotes

1  The Evolution of Data Lake Architectures | TDWI

Grant Smith

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