- Global public cloud services expenditure is predicted to reach $591.8 billion in 2023, illustrating an increase of 20.7%.
- The Cloud Data Warehouse and Modern Data Stack industries have grown exponentially due to current consumer demand.
- Organisations should remove data barriers such as silos and elevate the quality of their data to maximise their potential.
- Data observability can track how data flows through a system and ensure accuracy, completeness, and reliability.
- To reduce organisational resistance to change, leadership should paint a clear picture of the future and remove obstacles hindering transformation.
- Consultancies can help bridge expertise gaps between vision and execution.
Innovations in the Cloud Data Warehousing
According to Gartner, Inc., a technological research and consulting firm, spending on global public cloud services is poised to skyrocket, reaching a staggering $591.8 billion in 2023, signifying an uplift of 20.7%. This increase in spending represents an accelerated growth rate compared to the 18.8% growth forecasted for 2022. Cloud Infrastructure primarily drives this growth.
This significant upturn put spending globally at $490.3 billion last year and provided a surge in consumer demand to stimulate further investment. This increased demand signifies that organisations are increasingly moving infrastructure to the cloud, and organisations can trust that the transition will yield a return on investment.
In 2021, Fivetran, a platform for integrating data, obtained $565 million in a Series D funding round. This funding round increased the company's value to $5.6 billion, more than five times its previous valuation of $1.2 billion, which it had a year before the fundraising round.
Despite economic pressures to navigate, this growth indicates expansion in the Cloud Data Warehouse and Modern Data Stack industries.
98% of CEOs also prepare for a recession in the next 12-18 months. Their operations have shifted towards efficiency across all business areas rather than rapid growth, and cloud infrastructure optimisation is crucial.
Removing Data Barriers
To fully utilise the Cloud Data Warehouse and Modern Data Stack, organisations should aim to eliminate data barriers. The critical components of this process include elevating data quality, addressing organisational resistance to change, bridging expertise gaps, and consolidating silos.
Removing Data Silos
Organisations often face the issue of siloed data, wherein important information is separated and isolated in specific departments or applications. The first step towards breaking down data silos is to recognise the existence of such data silos and understand how they impact organisational objectives.
The most effective solution lies in a unified data infrastructure that combines all data sources in one centralised location, making it easily accessible among different teams. This initiative will benefit businesses in two ways:
Firstly, it will encourage collaboration between various departments, paving the way for enhanced decision-making and faster resolution of problems. Secondly, it will provide organisations with a single source of truth, thus enabling the creation of accurate and consistent insights that propel growth.
Businesses can implement a central, consolidated dataset to overcome obstacles and reach their full potential. This approach can help them break down barriers and the performance of the business.
Elevating Data Quality
"Data Observability" and "Data Quality" are often used interchangeably, which leads to confusion. Both involve data; however, the two concepts differ in that data observability tracks how data flows through a system, while data quality relates to its accuracy, completeness, and reliability.
These four pillars of data observability - metrics accounting for internal data characteristics, metadata for external factors, the lineage for data element dependencies, and logs providing interaction history with the outer world - are used to depict the data state at any given time.
The four pillars are required for a holistic view of the data state; lacking one provides an incomplete picture. Organisations can benefit from data observability by understanding the impact of changes and locating problems before they become critical.
Data quality measures how accurate, complete, and reliable a dataset is. The four critical components of data quality are accuracy, completeness, consistency and timeliness.
Poor data quality can cause enormous problems for teams, with lost revenue, inefficient operations, and bad decisions being just a few consequences.
To fix data quality issues, teams must identify essential dimensions, track metrics related to those dimensions, and set service level agreements (SLAs) to stay on track.
It's vital to measure what matters and make metrics actionable by connecting them to business outcomes and presenting them in a digestible form.
Addressing Organisational Resistance to Change
In 2018, Gartner conducted a survey which revealed that 87% of businesses feel it's essential to improve their Business Intelligence (BI) abilities and analytics maturity. However, these organisations need help to achieve this goal because they need more skills and expertise.
Organisations often struggle to execute successful transformations due to a range of factors, according to a 2009 Harvard Business Review article.
Among the most significant elements is a need for more urgency and failure to share the vision for change. When leadership fails to paint a compelling picture of the future, it is challenging to create buy-in from the rest of the organisation.
Additionally, removing obstacles to that vision is crucial to prevent resistance from creeping in, derailing efforts to achieve transformation. Identifying and overcoming these roadblocks are essential to smooth the path toward growth and success.
When it comes to the adoption of new ideas, and it results in friction from management or colleagues, we believe that they don't naturally have unkind intentions. Underlying insecurities and a lack of understanding might be causing conflict in adopting new ideas. Organisations should adopt a culture of having decisions challenged healthily. Furthermore, individuals should embrace the possibility of being wrong and challenged.
Bridging the Expertise Gap
Data consultancies offer value in three areas: providing organisations with strategic direction in terms of their goals, offering embedded expertise for implementing those goals, and assisting with onboarding and training the organisation's team to take over the project when needed eventually.
"Embedded teams provide the dedicated expertise necessary to bridge the gap between vision and execution, allowing organisations to unlock value in their data that may otherwise have remained inaccessible.
With this approach, businesses can ensure they get the maximum benefit from their data while avoiding costly mistakes or delays due to a lack of expertise.
In a recent case study, LIFT Airline invested in a cloud data warehouse, leveraging Fivetran and dbt to extract, load, and transform data, Snowflake for the warehouse itself, Metaplane for observing data, and QlikView for their reporting layer.
Consolidating their 30+ data sources has been instrumental in streamlining their reporting and reconciliation processes and gaining actionable insights into customer behaviour which has positively impacted their bottom line.
This successful journey exemplifies how companies can benefit from modernising their analytics and business intelligence operations.
Stealth Mode HealthTech
During Q3 of last year, we started a project with a healthcare start-up operating secretly. As they progressed, we observed considerable changes in their data regarding quantity and structure. Our ability to recognise these changes and adjust to new data flows across their many integration points has been essential in leading our team to success and assisting the rapid development of the product.
Our ability to stay ahead of the curve in this ever-changing landscape is a testament to the importance of data agility in today's business landscape.
Partnership with Metaplane
We have been long-term fans and customers of the products offered by Metaplane, the Datadog for data. We consider ourselves advocates for implementing Data Observability in every project as early as possible. The benefits outweigh the costs, with organisations getting increased transparency, better awareness of data issues, and improved trust in the data.
Organisations should also remember that Data Observability is not only a requirement for large enterprises. With modern Cloud technologies, everyone can benefit from it – including medium-sized businesses, start-ups and SMEs alike.
The cloud data industry is expected to experience astronomical growth outside the current economic environment, and organisations should take the opportunity to use this trend to their advantage by investing in the best technology for their needs.
To reduce resistance to change, organisations should provide leadership that paints a clear picture of how data transformation will work towards meeting the goals and objectives of the business. Where gaps in interpretation or execution arise, consultancies are available to help bridge the divide between vision and successful implementation.
To make the most out of this digital revolution, contact someone at Horizon Data to discuss your data challenges.