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Data Management Strategy Guide

How to incorporate an External Data Platform into your data management strategy

November 24, 2021 Data Acquisition

Organizations today focus on generating insights based on their internal data sources such as their CRM tools and data warehouses. To most organization's, the value of data that comes from outside their four walls is clear, however finding the right external datasets can be a challenge. The amount of big data available is overwhelming; it is hard to know what to look for, and which data sets will add value. Even if you know what type of data you need, external data can be expensive to purchase and difficult to access, format, and integrate with existing internal data. Overlooking external data is a missed opportunity as it provides important context not always captured in internal data: economic trends, consumer preferences, weather, reviews, social media trends, competitive intelligence, and more. Successfully leveraging external data creates a competitive advantage. Tools like external data platforms address the challenges of external data acquisition, and streamline the process of purchasing and integrating external data signals. Along with  new data warehouses and data lakes, an external data platform is becoming an essential part of the modernization of data architectures. Prior to onboarding an external data platform, it is important to incorporate external data into a data management strategy. This will establish how any new data and tools will fit into an organization's data architecture. A new tool will always be the most impactful when there is a purpose and a plan established prior to onboarding it. Creating an external data strategy helps establish and clarify the purpose, and define the methodology to implement external data into an organization's data models.

This blog post will go over the steps to incorporate an external data platform into your data strategy: 

  1. Define your enterprise data strategy
  2. Build your team
  3. Define and prioritize business

Step 1: Define Your Enterprise Data Strategy

A successful data strategy means turning data into actionable insights that drive decision-making processes. Forward-thinking organizations understand that having the right internal and external data is vital for success. 

External data adds value to an organization’s data pipelines, yet there are several challenges to leveraging it successfully. Our own survey found that even though organizations overwhelmingly indicated that the acquisition and onboarding of external data was important to their business, less than a third of respondents actually had a strategy in place, with 26% relying on ad-hoc practices or an informal process for data acquisition. 7% of respondents reported that they found data acquisition challenging enough to not do it all. 

A data acquisition strategy doesn’t happen overnight. Building a “data hunting” organization — one that actively seeks out data opportunities — requires that you put the right resources in place and build the right teams with platforms & technologies to support them.

A major challenge of scaling data acquisition initiatives is that too much variety creates paralysis. There is an increasing amount of data providers selling large amounts of data. Managing multiple external data sources at the same time and manually performing data analysis is time-consuming. The process of testing every data asset in the hopes that it might be relevant for each project is not scalable. Formulating a plan will help ensure that money is not wasted purchasing expensive datasets, and that the data purchased is turned into actionable insights driving better business decisions.

A data strategy should outline a business' needs and capabilities for accessing external data. It directs the company to review and prioritize use cases where external data sources will have the highest positive impact and ROI. 

Step 2: Build Your Team

A good data strategy should help data stakeholders and teams become more cohesive, specialized, and organized. On data teams, it is common to see an overlap of job roles, task silos, duplicate processes, and general operational inefficiencies when dealing with large volumes of data.  

Your organization likely has an existing relationship with an external data provider such as Dun & Bradstreet, Zoominfo, Epsilon, Refinitiv, or others. But these relationships are typically for a single business purpose and don’t scale across the organization. Using external data effectively and at scale requires a new way of thinking about partnerships, a new way of collaborating among business and technology teams, and a new pace of coordinating across the various teams. This is where having a centralized organizational model is critical. You can call it an external data team and think of it as a project management office for external data. The charter of this team is to drive and expand the use of external data within your organization, identify relevant use cases where external data brings more value, and ensure its use is aligned with the strategic initiatives of your organization. The management consulting firm McKinsey has identified 6 key roles that need to be part of this team: 

  1. Data scouts/strategists 
  2. Data reviewers 
  3. Purchasing experts 
  4. Data scientists and analysts 
  5. Architects and DevOps engineers 
  6. Data engineers

It doesn’t require a huge team, you just need to cover these key roles (a person can fill more than one role in some cases). The right technology will also greatly assist these roles, connecting people and processes.

Step 3: Define and Prioritize Business

It is important to understand the business value of external data in order to identify relevant types of data signals prior to embarking on a data acquisition journey. Organizations that are using external data sources see tremendous value by incorporating it into their data science programs, data analytics, and predictive models. Businesses use external data to personalize marketing offers, optimize customer acquisition tactics, prioritize incoming leads, enhance risk and fraud visibility, and anticipate shifting consumer trends. Data scientists and analysts will be able to measure and identify the improvement and uplift in their predictive models by including external data. This helps develop the appropriate KPIs to define and track progress on delivering business value. It also helps identify the use cases where external data will help the most.

The following are questions to ask to help guide the design of your KPIs related to both business impact and organization readiness: 

Business impact: What is the measurable impact of external data on business goals and customer needs? What is the impact of using external data versus doing nothing and keeping things the same? How much will external data contribute to the organization’s goals?

Organizational readiness/level of effort: What are the technical impediments to using external data? These could include incomplete data sets, errors in population specification, sample frame error, selection error, or even observational error. What are the process impediments to using external data? What is the readiness of the people to use external data? These could include changes to definitions and policies, inconsistent data collection standards, lack of data quality assurance, and so on.

 

Tying it all together; finding the right enabling technology

After creating a data strategy, it is easier to understand what the right technology solutions are to help achieve the strategic goals and business objectives that have been laid out. Many organizations at this milestone direct their attention towards external data vendors whose scope is akin to a data catalog. 

Choosing an external data platform as opposed to data vendors or data marketplaces will take data collection to the next level. An external data platform can help streamline the data acquisition and integration process while consistently providing up-to-date data for the organization's data needs. The platform serves as much more than a repository of information, has data science/ML (machine learning) capabilities, and enables:

  • Data access
  • Dataset generation
  • Data preparation
  • Data enrichment
  • Feature engineering
  • Modeling selection & testing
  • Model deployment/operationalization
  • Data security & compliance

An external data platform automates data access, and cleans and harmonizes datasets for use in machine learning or data analytics projects. It provides ways to automate data integration, making it easy to visualize and use data. These capabilities remove roadblocks in finding and acquiring data sources, making it easier to streamline the data pipeline and successfully deploy models. 

Include Explorium in Your External Data Strategy

Access External Data wit Explorium

Unearthing valuable business intelligence rests on how efficient a data team is at organizing unstructured data into data for easy visualization. Explorium is an External Data Platform that connects your data people and processes and automates connections to hundreds of pre-vetted data sources  and thousands of individual data signals. The sources are curated for quality and reliability and form a single collective catalog. The platform eliminates the need to match and integrate each data set separately. With Explorium, you can choose which data signals to incorporate into your existing data pipelines.  

To learn more about incorporating external data into your data plan, check out our "Strategy Guide to Implement an External Data Platform".

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