Operational Analytics: Bridging Gap Between Data and Operations Teams

• July 23rd, 2022

Data and Operations Teams- Featured Image

In the last couple of years, the data environment has grown at a breakneck rate. For data-driven organizations, every business function and every department is dependent on data. They are aware of tangible outcomes like increased efficiency and better business outcomes. As more and more data becomes available, operations teams become more dependent on data teams, resulting in a growing gap between data teams and operations teams. 

If left unchecked, this problem will worsen, making it much more difficult for data teams to allocate their resources and time effectively. Your data teams have a purpose and an objective – they should focus on high-value, strategic work, instead of working as service-based departments, where operations teams submit their tickets with a request and data teams reply with a specific answer.

For organizations where a centralized data team structure is followed, the problem gets worse. Non-technical professionals, who comprise a major chunk of the user base, and lack coding skills become increasingly reliant on data teams for their day-to-day tasks. They have to ask data teams for reports, and sometimes raise requests frequently to obtain real-time insights, leading to wasted efforts and bandwidth of those data teams.

Operational analytics aims to make it simpler for business analysts and even non-tech professionals to leverage data insights in their own systems; one they are familiar with while carrying out their daily business operations. Real-time delivery of insights helps reduce the dependence and gap between data and operations teams. We believe using operational analytics, your data and operations teams can shift their focus from sending and receiving data packets to making productive actions. Have a look at how you can make it happen for your organization.

What does a Data Team do?

In most organizations, data teams are responsible for fostering a culture where everyone buys into the idea of better business decision-making. They drive and deliver technology that enables easy data accessibility and self-service. 

Have a look at the six different functions that data teams perform:

  • Data Engineering: Data engineers design, manage and optimize the flow of information. They ensure that your data pipelines are operational and that other data professionals like data analysts and data scientists have access to relevant data. 
  • Data Warehousing: Data professionals are responsible for data warehousing activities that integrate data, store business logic, and prepare data for consumption.
  • Database Administrators: Database administrators are responsible for planning, maintaining, and managing their company’s databases.
  • Analytics: Analytics team leverages warehouse data to generate interactive charts and dashboards for line-of-business owners to use in reporting.
  • Data Science: The data science team works to improve the product, maybe by developing sophisticated features such as search, recommendation, or matching algorithms.
  • Growth/Ops: The growth/ops team will collect data at all levels (current dashboards, warehoused tables, raw data) and create initiatives for particular lines of business to boost revenue or efficiency.

How does a Data Team Integrate with Other Teams?

Centralised Data Team: Data and Operations Teams
Image Source: Castor

Data teams are in high demand. A strong central data team has the responsibility to provide information and drive decision support across different departments in the organization.

For most organizations, a data team acts as a central hub where their primary task is to make data available to everyone, irrespective of their function or division. Marketing, finance, supply, product, and sales teams depend on data teams who can understand datasets and help them find the best answers to their questions. Members of the team with such responsibilities have a duty to convert system knowledge into business knowledge. 

The centralized data team model enables businesses to produce feasible data with the least amount of complexity. Let’s have a look at the benefits provided by having a central data team in your organization:

  • A central data team can support other teams while focusing on its core business tasks – it is a flexible model that can adjust to the changing demands of an organization.
  • A centralized data team model serves as the foundation for the data team to concentrate on long-term projects while also assisting surrounding teams.

What does an Operations Team do?

The operations team of a company is made up of employees who assure clients’ happiness and contentment while providing the services they require. They are in charge of ensuring that the company’s hard-earned consumers are taken care of. Let’s look at the advantages offered by operations teams in your organization.

  • Timeliness: The operations team is constantly available for client communication, ensuring that their services are delivered on time and that the customer is happy. In most industries, rapid delivery of services and solutions is critical for businesses to retain clients.
  • High-class Standard: Having personnel devoted to providing the greatest services for your clients elevates your organization to a higher level of excellence. That is, the business develops a positive reputation among consumers, which is often a deciding factor for future clients or partners.
  • Better Productivity: The operations team enables you to do as much as possible in a short period. They understand your company’s primary operations, the kind of solutions you may offer, and your talents, and they don’t waste time.

Let’s look at the three operational team divisions:

Marketing Operations

Marketing operations are concerned with the procedures, technology, and resources needed to grow marketing and to make day-to-day initiatives more efficient. It oversees technical infrastructure, data hygiene, reporting, and other aspects to enable the scalability of operations and campaigns.

A marketing team with an eye on operations implements automation and procedures to scale sustainably without jeopardizing customers’ experience. This allows them to handle a growing amount of marketing interactions as a company grows.

Sales Operations

The aim of the sales operations is similar to those of marketing operations. It improves systems, procedures, technology, and infrastructure to assist sales teams in selling more successfully and efficiently.

Sales teams are busy, and sales operations assist these fast-paced teams in being organized and, as a result, selling more. SalesOps helps ready a sales force for future development by providing good reporting, reducing friction for salespeople and prospects, and implementing new technology.

Service Operations

Service operations entail working with professionals, customer success, customer support, and customer experience teams. Service operations strive to support and magnify a team’s capability while also assisting them in scaling. It necessitates balancing the interests of internal stakeholders and external clients.

Most businesses may not have a specialized service operations team to handle day-to-day service operations. Instead, service operations experts may be allocated to the company’s business operations or revenue operations teams.

In addition to infrastructure maintenance, service operations will assist in capturing and reporting metrics related to the teams they support, such as time to resolution, tickets closed, how well services are delivered, team efficiency, customer usage of offerings, and the ROI of all of those functions.

Bridging the Gap Between Data and Operations Teams

Businesses realize how powerful and transformative their data and operations teams can be. They know the tangible results that come when operations teams have the right datasets, provided by data teams. These can then be used to drive positive business outcomes.

While these positive results showcase growth, they curtain over the fact of dependency. We aren’t saying that dependencies are bad. Healthy levels of dependency foster collaboration, and growth, and bring different perspectives and ideas. But it becomes terrible when operations teams are heavily reliant on data teams for day-to-day tasks. 

Think about this situation for a moment. It is the festive season, and your sales are skyrocketing. Your marketing team has requested data for the recent performance of the personalized campaign they delivered to your target segment. Your customer support team at the same time has requested data on the new customers, so they can offer exceptional customer service and educate them. Your sales teams want real-time data on the number of leads you are getting per hour, so they can take proactive actions…and your business has delivered the perfect condition to make your data teams dazed.

So, how do you overcome such challenges? Let’s discuss.

Establish a Single Source of Truth

Creating a single source of truth for your data and operations teams guarantees that business teams operate on a consistent and unified dataset. Data sets exist in silos without a single source of truth, and each department works as a black box. Implementing a single source of truth allows business executives to make data-driven choices based on data from the entire organization rather than compartmental data silos.

Customer and service data, sales and marketing conversion rates, and much more may all be used to guide company decisions. To compete in today’s data-driven economy, businesses require holistic insights. Having a single source of truth for your organization eliminates reliance on data teams to deliver data. Business analysts in their respective teams may readily query and access data, and at the same time support their team members to proactively take actions without having to ask data teams.

Get a More Accurate View of Data 

A 360-degree picture of customer data is consolidated information of a customer’s engagement and journey with an organization. A 360-degree view of customer data allows data and operations teams to give consumers the most comprehensive experience possible across all channels. It enables the organization to link many touchpoints, allowing them to provide excellent customer service at every stage of the process.

Data and operations teams need a 360-degree view of their consumers’ data to provide the best service possible. Companies utilize a range of tactics, including customer experiences and feedback, to gather soft and hard consumer data.

Implement Systems Correctly from the Start 

A data team may break down data silos by ensuring that each new technology can pull the correct data in the right way. With the help of ETL/ELT, data teams can extract the correct data, transform it and load it into a data warehouse and make it readily available for all. With this set of actions, data can be accessed and queried accordingly by respective team leads/analysts without asking data teams separately; hence reducing their dependency.

Make Insights Available to Operations Teams 

Your move to centralize data is just not enough to bridge the gap between data and operations teams. There is one more aspect to it. 

The centralized location that you chose to remove data silos has the potential to in turn itself become a data silo. This happens when you focus on integrating data, rather than making it actionable. Your set purpose with the use of a data warehouse or database is to store information and serve as a repository for operationalizing data throughout an organization. Without this, there is limited access to data for non-tech people, since they cannot run SQL and retrieve data from the warehouse. All this led to the loss of prospects, poor customer engagement, poor marketing campaigns, etc. 

Businesses have now realized this problem and have started embracing a new methodology called reverse ETL to tackle such an issue. Reverse ETL is a process to achieve operational analytics, an approach that enables non-technical folks in your business teams to have unfettered access to customer and business actionable in real-time that can be readily incorporated into daily decision-making.

With the help of reverse ETL methodology, data teams can send data back from the data warehouse to the business applications. There are plenty of benefits of using reverse ETL, and a few of them are:

  1. The marketing team can create better customer engagement, hyper-personalization, and real-time analysis of ongoing campaigns.
  2. The sales team can find leads, and nurture them as soon as they sign up on your platform.
  3. Support teams can prioritize the support tickets, etc., and provide the best customer support in a timely manner.

Make Better Business Decisions

Once the real-time data reaches your daily business applications, you can leverage that data to create targeted campaigns, create real-time lead scores, prioritize the support tickets, and so on. Operational analytics enables data and business teams to get real-time, comprehensive insights and drive actions. This aids them in producing more efficient workflows using automation, improved communication, and timely actions.

Transform Company Culture

Making better use of data is like a team sport – data and operations teams have to work hand-in-hand to deliver exceptional business outcomes. And when suitable requirements and systems are in place, your teams are able to produce their functions without being too much reliant on data teams. 

You promote a culture of autonomy, accountability, and agility. This ensures that team members are heard and that they are involved in the process of decision-making. This improves the business efficacy of your operations and hence profitability.

Empower Business Teams with Data-driven Actionables using Hevo Activate

Hevo Activate syncs data from your warehouse – Snowflake, Redshift, BigQuery – into Business Applications – HubSpot, Salesforce, Zendesk, Google sheets and many more. Check out our growing list of integrations.

Hevo Activate features –  SQL Query Editor, Custom Data Synchronization, Incremental Updates, Intelligent Data Type Conversion -helps business teams get accurate data without writing complicated Python Scripts. 

Make faster and smarter decisions by operationalizing your product and customer data together – analyzing user journeys, and creating truly personalized experiences for your customers.

Enrich your applications with trusted data in your warehouse. Get started with Hevo Activate for reverse ETL today!

Get started for Free with Hevo Activate!

How does Operational Analytics Unite Data and Operations Teams?

For long, insights have been kept in the business reports, allowing business leaders to make decisions and strategies. Data teams can easily access data anytime they want. But with the technical constraints among operations teams, they depend primarily on data teams for these reports, which are not built in real-time. This leads to delays in taking action, and operations teams might lose a valuable prospect.

Data teams’ biggest challenge is not having real-time access to data. With the help of operational analytics, teams can develop optimal and realistic recommendations in real-time based on trusted, highly-accessible data from your warehouse. Reverse ETL creates a single source of truth by operationalizing your data and transferring insights from the data warehouse to familiar operational systems, where operations teams can use them as part of their typical workflow. This keeps a real-time data sync among data and operations teams, allowing them to break technical knowledge barriers.

Another challenge that data and operations teams face is taking action on those data. With data available in the reports, taking action on them becomes a tedious task since everything has to be done manually. With the help of reverse ETL, you can operationalize your data and send it to the applications you use daily and can take actions in real-time.

Another challenge that data and operations teams face is having a complete view of customers. Data in the different applications provide a limited view of customers to the different teams. Therefore, applications themselves become data silos. With ETL/ELT data and business teams get to combine data from several sources, consolidate it into a data warehouse, from where it can be synced into operational systems like Zendesk, Salesforce, Marketo using reverse ETL. All the teams get a comprehensive view of their customers in their daily applications.

Reverse ETL: Operationalize Your Data with Ease

Reverse ETL: Data and Operations Teams

As discussed, data warehouses can sometimes fail to tackle data silos issues. Your critical business KPIs may be separated in your data warehouse, preventing non-tech personnel from properly leveraging your data. While employing standard ETL, these teams rely significantly on your data teams. When they want relevant insights, they must seek a report from data analysts. Similarly, when a new SaaS service is integrated into a customer’s workflow, they rely on your data engineer to design unique API interfaces.

These issues may slow down and limit data availability for your front-line business users. Reverse ETL technologies provide operational and business platforms with real-time data (like Salesforce, Intercom, Zendesk, MailChimp, etc.).

Reverse ETL is the process of changing your data warehouse into a data source and your operational and business platforms into data destinations. Making data accessible to these platforms may give your front-line workers a complete view of customer data. Personalized marketing campaigns, intelligent ad targeting, proactive customer feedback, and other applications may all benefit from data-driven decision-making.

Let’s take a look at some of the most important advantages of reverse ETL:

  • Data Democratization: Data teams may employ reverse ETL to give data insights to other operational business teams as part of their routine. Data becomes accessible and actionable since it is sent straight from the data warehouse to platforms like CRMs, advertising, marketing automation, and customer support ticketing systems.
  • Automates Data Flow: Reverse ETL eliminates the time-consuming process of traveling between apps to gather information. Reverse ETL frequently transmits vital KPIs and metrics to operational systems. This enables it to automate a wide range of processes.
  • Saves Data Engineers’ Bandwidth: Because of the built-in API connections, reverse ETL saves data engineers’ time doing manual labor. This frees up a lot of their bandwidth and allows them to focus on more critical activities.

How Clearbit Uses Reverse ETL to Empower Their Operations Teams

Clearbit Logo: Data and Operations Teams
Image Source: PR Newswire

Clearbit is a SaaS company that creates business intelligence tools to assist companies in gathering more customer information, enhancing sales, and decreasing fraud. Clearbit faced challenges as their data was not synced among data and operations teams. Their main challenges are as follows:

  • There was no single source of truth. Customer and product data were kept separate in various tools.
  • No single customer view resulted in duplicate users across their 6+ products.
  • For their data business professionals, observing the entire client journey and assigning lead scores was impossible.
  • They relied too much on engineering and IT teams to bring data into business tools via customer integration.

With the help of the reverse ETL platform, they were able to solve their pain points:

  • They were able to achieve a 360-degree customer view in Salesforce, allowing them to increase the productivity of account executives and managers.
  • Email marketing got hyper-personalized based on product usage, acquisition channels, customer journey stage, and customer success.
  • More teams were able to use Clearbit’s data.
  • Data was no longer limited to commercial ideas/campaigns.

How does Hevo Activate Bridge the Gap Between Data and Operations Teams?

Hevo Activate: Data and Operations Teams

Consider the following scenario: suppose you’ve been presented with a business challenge, and your team has been given a month to come up with a solution. You spend time cleaning data, creating models, and refining information into visuals based on the project’s basic specifications. After a week or later, you finally submit your work to the business team. Your confidence is great, but it rapidly fades when your business team says flatly, “Great! Regrettably, the customer preferences and market scenario have changed. “Can you perform the same thing with new data?” You’ve squandered 2-3 weeks of work and time, and it’s time to start over.

Such problems waste all your efforts and time and reduce your morale. With the correct tool in your organization, you can perform your analytics in real-time, and nontechnical teams can also take action with that data. 

With the Hevo Data No-code Data Pipeline, you can extract data from multiple sources to your warehouse. Then, using Hevo Activate, you can operationalize your data by sending it back to the daily use applications. This created a bidirectional data pipeline, allowing you to close the modern data stack loop. This will help your data and operations teams to work and take actions on data in real-time without the need for coding knowledge. 

Here are the steps required to configure and set up your reverse ETL data pipeline with Hevo Activate.

  • Step 1: Connect data warehouse and destination application
  • Step 2: Write a query to get relevant data
  • Step 3: Map the output to the destination object

Let us look at some of Hevo Activate’s key features:

  • Real-Time Data Replication: Hevo Activate’s excellent interaction with numerous data sources enables you to send data rapidly and effectively. This guarantees that bandwidth is appropriately utilized on both ends.
  • Secure: Hevo Activate’s fault-tolerant design ensures that data is handled accurately and reliably, with no data loss.
  • On-Demand Sync: Hevo Activate allows users to finish data sync by continuing or executing sync on demand.
  • Intelligent Data Type Conversion: During the mapping procedure, Hevo Activate automatically converts the field types of the synced data.
  • Data Transformation: Hevo Activate provides a straightforward interface for refining, changing, and improving the data you want to upload.
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Final Thoughts

Clear communication between data and operations teams has become the need of the hour for most organizations. For example, in most companies’ technical people do not know business requirements such as scope, pricing, deadlines, data formats, and visualizations. However, operations teams must understand where the data is coming from, if it is replicable, the data pipeline, and how frequently the data must be updated. 

The difficulty is that both profiles employ distinct tools, and procedures, and have different expectations. The worlds of Python, R, and Spark are very different from PowerPoint, Word, and Excel. 

All such problems lead to delays in action and losing valuable prospects. This problem can be solved by a bi-directional data pipeline platform such as Hevo. With Hevo, you can ingest data from multiple sources to your desired destination, where data people can find valuable insights which can be further sent to the operations teams’ daily use applications. This enables your operations teams to make judgments and take action on data in real-time, increasing the top line while decreasing wasted effort.

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