Solve Your Business-Critical Data Challenges with Snowflake

November 15, 2022
Justin Lumiere

Globally, corporate executives concur that it's critical to find solutions to data challenges. However, most businesses are still in the planning or implementation stages of their data governance programs. In a 2020 Dataversity report1, only 15% of businesses had fully implemented their initiatives, while 41% were still in the planning stages, and 33% had just begun.

Snowflake can assist you in discovering fresh innovations in data engineering that can boost the efficiency of data teams and provide more data self-service for analytics teams. The obstacles associated with resource limitations may be overcome with the help of increased efficiency and self-sufficiency.

Many organizations are experiencing financial constraints and even reductions as a result of the present economic climate. The same is true for data and analytics teams. In order to control costs, particularly cloud expenditures, these teams are expected to accomplish more with less just like the rest of the corporation.

The majority of data and analytics teams previously had a lack of employees and still do now. In this cutthroat industry and given the dearth of data engineers, it has always been difficult to find qualified data specialists. However, given the state of the economy, data teams must continue to provide more data to business teams.

The Snowflake data lake services platform offers an opportunity for data professionals to discover methods and strategies to make better use of their resources and prevent their cloud costs from skyrocketing.

All in an effort to promote the use of analytics and keep bringing more data to the business more quickly.

That's because businesses frequently encounter obstacles while using data. Here are a few typical challenges that businesses encounter. To learn more about how Snowflake's cloud data platform can assist with overcoming these difficulties.

Data Opacity - Companies must achieve data transparency to answer the questions, "What data do you have and where does it reside?" How are they using it? Who has access? But the solutions to these problems are hidden by antiquated systems. This type of data inventory requires effective data management, including a plan and procedures for accessing, integrating, storing, transferring, and getting data ready for analytics. Effective data governance "grows out of data management maturity," claims Forrester Research3. However, poor data management is a problem for many firms.

Data Silo - Overcoming the silo mentality in business domains is one of the most difficult parts of data and analytics governance, according to a recent Gartner survey2. Rigid legacy data architectures encourage data isolation by impeding information sharing and dissemination across the board. Companies find it challenging to clearly arrange information due to legacy infrastructures. Applying data governance, which includes tracking data lineage, cataloging data, or implementing a granular security architecture, is impossible with siloed, disorganised information.

Insecure Data - Data breaches are on the rise, along with the growth of data sources inside and outside of businesses. For data security to be improved, data governance is essential. Data security relies on traceability, or understanding where your data comes from, is, who has access to it, how it's used, and how to remove it. This is similar to how good data management relies on traceability. Data governance establishes policies and practices to guard against data leaks that could expose private customer or corporate information to unauthorised parties. Legacy platforms, however, produce segregated data that is hard to access and track. It becomes even more difficult to track down where all the data went because these silos are frequently exported, sometimes to spreadsheets, and copied to merge them with other siloed data.

Lack of Data Control - Companies frequently start considering data governance when they must adhere to legal requirements such as GDPR, HIPAA, PCI-DSS, and the U.S. Sarbanes-Oxley (SOX) statute. According to the Dataversity report, 51% of businesses cited regulatory compliance as the main reason for their data governance practices. Organizations must be able to track their data from its inception to retirement, determine who has access to it, and understand how and where it is utilised in order to comply with these rules. Data ownership and accessibility are governed by rules and processes. Without it, confidential material may fall into the wrong hands or be inappropriately deleted, resulting in monetary fines from the government or regulatory bodies, legal action, and possibly even jail time.

Poor Data - Data governance involves monitoring both the quality of the data that enters a corporation and how it is used there. Data stewards must be able to spot instances where data is erroneous, outdated, corrupt, or being evaluated out of context. They must be able to quickly establish procedures and rules. A fundamental requirement for data-driven companies that rely their choices on data from numerous sources is the capacity to trust data.

According to 60% of businesses in the Dataversity survey, one of the biggest bottlenecks in their organization's data value chain is not fully comprehending the quality of the source data. The research states that "documenting lineage down to the column level and automating and matching business terms with data assets are essential steps to improve data quality."

Overcoming Resource Limitations - The largest problem that every data team faces is a lack of resources. Budget restrictions will only make this issue worse. Additional data requests from business teams make the requirement for faster delivery and constrained resources even more difficult.

Data modeling and transformation systems that combine SQL and no-code tools, have built-in data catalogs, provide reliable deployment techniques, and promote cooperation between data and analytics team members are one new innovation you will see at the Snowflake. These will be improved with Snowflake-native tools.

These new platforms for data modeling and transformations help address resource constraints by:

Increasing the efficiency and productivity of data engineering teams through quicker data pipeline design, component reuse, and quicker change management.

Expanding the pool of data processing resources by bringing on non-coders who can model and convert data, allowing data and analytics teams to work together to improve workflows, and removing communication barriers that often snag projects.

Maintaining Control Over Cloud Costs - Costs in the cloud will inevitably increase over time, especially with cloud data warehouses like Snowflake. Costs will rise as the organization receives more analytics use cases and data as usage increases. But due to financial restrictions, certain teams might be required to keep costs in check or at least at a level.

Engineers can reduce expenses by finding ways to optimize, streamline, and improve their data pipelines using snowflake data. To further aid with cost containment, data professionals might uncover chances to streamline their data stack around Snowflake.

By swapping out antiquated data transformation tools for contemporary, cloud-native ones that are more productive, streamline processes, and produce faster-delivering data pipelines, utilizing data transformation capabilities inherent to Snowflake that are optimized for Snowflake's computing and storage for lightning-fast performance and effective resource use, and

Wrapping Up

Using solutions for data transformation that keep all data models and datasets in Snowflake, you can do away with expensive additional data silos and the administration costs associated with maintaining them.

The cloud data platform from Snowflake offers the ideal framework for programs that remove data barriers. Snowflake includes capabilities that help businesses accomplish compliance and improve decision-making by utilizing secured, controlled data. It also assists businesses in destroying data silos. The three main data clouds, elastic storage, elastic computing, data encryption, access restrictions, tracking capabilities, and integration with third-party data management tools are all included in this.


Leave a Reply

Your email address will not be published. Required fields are marked *

linkedin facebook pinterest youtube rss twitter instagram facebook-blank rss-blank linkedin-blank pinterest youtube twitter instagram