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It’s time to get practical.
The first article in our Data Imperative series explained the 3 truths of data:
The second tackled how to overcome the 3 biggest data challenges businesses face:
We’ve established the reasons to get into the car and identified the obstacles to avoid on the way. We know that strategy, technology, process, and people all need to be in the car. But driving forward without directions is ineffective at best and disastrous at worst. To navigate a successful data transformation, you need a roadmap.
The 5 steps of data transformation include:
Read on for a detailed description of each step and how it takes an organization from data zero to data hero.
All functions of a business involve interaction with data, but most businesses don’t do it strategically. Marketing departments sift through social media data, sales teams dig through endless excel sheets, HR oversees employee details, and leadership looks for improved insights to manage it all. Beginning a transformation requires Step 1: creating a vision and strategy for the future of the org’s data.
At the beginning of the Strategize stage, consider questions like these:
To effectively map a data journey, it’s important to pinpoint the specific processes needed to meet high-level goals as they apply to the collection, storage, and usage of company data. This means implementing a strategy for reliable and trustworthy systems that can be duplicated with confidence over the course of the journey to data transformation.
High-quality data enables better decision-making, but there are other reasons to overhaul a data approach. By revising a data blueprint strategically, an organization looks not only at improving the current state of data, but the future state of data as well.
Because the data lifecycle is circular, a detailed data strategy allows an organization to continuously improve metrics as they evolve over time:
Data strategy is and will continue to be the first step to a data-transformed organizational culture. Without it, teams may be able to collect the increasing influx of data, but they cannot master it.
After creating a data strategy, companies can move on to accessing and storing the vast, diverse, and unstructured data available to and across the organization. Stage two on the data roadmap: creating a digital data lake to capture data.
Data lakes are essentially digital vaults for extensive data storage, used to offer an unrefined, real-time view of data that has yet to be parsed or processed. In this stage, data is stored as is, with no formalized procedures or analytics to run before observation and use. Data lakes can handle data from various sources, including IoT, mobile, web, social media, and enterprise applications.
This data reservoir allows data scientists, developers, and analysts to extract and analyze applicable datasets for company use. In addition to data extraction, this information can be used to model outcomes based on historical data—making it all the easier to leverage technology such as robotic process automation (RPA) and machine learning (ML) to forecast the trends and bolster better decision-making for business growth and new opportunities.
Here’s one example of using historical data to catalyze transformation:
Launch helped an industry giant in heavy civil construction develop a virtual assistant that applies historical knowledge to new projects via ML. This platform acts as a “network of knowledge” based on all previous jobs, and by flagging project elements that don’t align with the historical data, the construction company avoids costly mistakes, mitigates risk, builds more profitable bids, and manages projects more effectively.
A data lake can only take a business so far into understanding and action, especially if different people and teams can only fish in certain parts of it. To devise the insights necessary for a full digital overhaul, it takes Step 3 of the data roadmap: equipping the business with data that enables progress.
During the strategy stage, you considered how data is accessed and used among different departments. Often, departments work individually and with independent standards and procedures, working on responsibilities and goals that seemingly don’t apply to other parts of the organization.
Employees who are used to functioning within this limited capacity do a disservice to themselves and their organization—but the fault isn’t all their own. If your company operates with a competing vs. collaborative mindset, silos will continue to plague your organization’s development and limit vital decision-making and transformational initiatives.
No matter how much automation an organization applies, true insight must come from people. Equipping teams with the tools and knowledge to access, use, and—critically—protect company data enables them to break out of silos and leverage insights from multiple arenas to elevate the entire organization.
The lesson: equip teams with the data they need to chase down the big-picture goals. The data landscape is vast, but the power of an educated team is limitless.
Here’s where the fun of data enrichment starts: integrating data to augment existing services, create new ones, and become an industry pioneer.
Data integration acts as the meeting ground for data pulled from various sources. As more and more new data flows into your organization minute by minute, integration cuts down on the time needed to prepare and analyze data, and contributes to overall data continuity.
With a single, comprehensive view for consolidated data management, users can pull valuable insights regardless of type, structure, or size.
The benefits of integrated data include:
Using integration to bring real-world value became a reality for a telecom giant Launch partnered with. This company put their vast amount of untapped viewership data to use and built a new ad platform that made $2 billion in its first year—and was deployed in under 6 months. Their advertising data portal provides real-time insights for their customers so they can make intelligent decisions about advertising spend.
Integrated data is opportunity in data. It enhances operational processes, opens new doors, and puts an organization on the ultimate edge of innovation.
Steps 1-4 give a company what it needs to pioneer a data revolution, but turning employees into explorers is another thing altogether. That’s why the last step in the data roadmap is creating a data movement in your organization. That includes managing data and engaging people.
When it comes to managing data, organizations must contend with a range of regulations, security initiatives, skill gaps, and budget constraints. A handy companion to your data management strategy is business intelligence (BI). BI tools are used to make sense of the vast array of data flowing in and out of businesses. It is, in short, a digital aid to assist your teams in data-focused decision-making.
To ease your employees into a truly data-driven company culture, training is key. If your employees recognize the benefits of adopting data-centric practices for both themselves and the business, success is more attainable.
Earlier, we mentioned the competing vs. collaborative mindset often found in organizations. Imagine a marketing team conducts an in-depth survey of customer satisfaction. The results of the survey are mostly favorable, but the marketing team notices a high percentage of negative feedback based on a specific product function. The marketing team takes this data and uses it to craft the next iteration of surveys without sharing the results. In this example, the marketing team isn’t necessarily competing with other departments, but they are keeping their data in a silo.
Ideally, the marketing team would share the survey data with the product development team to address the negative feedback.
Getting your entire organization on board to become data-driven won’t happen overnight. But, when employees are given the tools they need to thrive in a digital-first landscape, early adoption is more likely, and success is imminent.
To review, the five steps for a triumphant data roadmap are:
But you can’t build a path forward without first understanding what step you’re on. Launch is proud to offer the Future State of Data Workshop, an interactive working session that envisions your next steps to transformation.
While orgs of all sizes and sectors can take this roadmap and start building a comprehensive data strategy, navigating with purpose to create that future state results in unique insights for your specific organization. Whether you’re on Step 1 or Step 5, crafting a plan to reach your destination will help you become a data hero for your business.
%402x.svg){kind=icon}
It’s time to get practical.
The first article in our Data Imperative series explained the 3 truths of data:
The second tackled how to overcome the 3 biggest data challenges businesses face:
We’ve established the reasons to get into the car and identified the obstacles to avoid on the way. We know that strategy, technology, process, and people all need to be in the car. But driving forward without directions is ineffective at best and disastrous at worst. To navigate a successful data transformation, you need a roadmap.
The 5 steps of data transformation include:
Read on for a detailed description of each step and how it takes an organization from data zero to data hero.
All functions of a business involve interaction with data, but most businesses don’t do it strategically. Marketing departments sift through social media data, sales teams dig through endless excel sheets, HR oversees employee details, and leadership looks for improved insights to manage it all. Beginning a transformation requires Step 1: creating a vision and strategy for the future of the org’s data.
At the beginning of the Strategize stage, consider questions like these:
To effectively map a data journey, it’s important to pinpoint the specific processes needed to meet high-level goals as they apply to the collection, storage, and usage of company data. This means implementing a strategy for reliable and trustworthy systems that can be duplicated with confidence over the course of the journey to data transformation.
High-quality data enables better decision-making, but there are other reasons to overhaul a data approach. By revising a data blueprint strategically, an organization looks not only at improving the current state of data, but the future state of data as well.
Because the data lifecycle is circular, a detailed data strategy allows an organization to continuously improve metrics as they evolve over time:
Data strategy is and will continue to be the first step to a data-transformed organizational culture. Without it, teams may be able to collect the increasing influx of data, but they cannot master it.
After creating a data strategy, companies can move on to accessing and storing the vast, diverse, and unstructured data available to and across the organization. Stage two on the data roadmap: creating a digital data lake to capture data.
Data lakes are essentially digital vaults for extensive data storage, used to offer an unrefined, real-time view of data that has yet to be parsed or processed. In this stage, data is stored as is, with no formalized procedures or analytics to run before observation and use. Data lakes can handle data from various sources, including IoT, mobile, web, social media, and enterprise applications.
This data reservoir allows data scientists, developers, and analysts to extract and analyze applicable datasets for company use. In addition to data extraction, this information can be used to model outcomes based on historical data—making it all the easier to leverage technology such as robotic process automation (RPA) and machine learning (ML) to forecast the trends and bolster better decision-making for business growth and new opportunities.
Here’s one example of using historical data to catalyze transformation:
Launch helped an industry giant in heavy civil construction develop a virtual assistant that applies historical knowledge to new projects via ML. This platform acts as a “network of knowledge” based on all previous jobs, and by flagging project elements that don’t align with the historical data, the construction company avoids costly mistakes, mitigates risk, builds more profitable bids, and manages projects more effectively.
A data lake can only take a business so far into understanding and action, especially if different people and teams can only fish in certain parts of it. To devise the insights necessary for a full digital overhaul, it takes Step 3 of the data roadmap: equipping the business with data that enables progress.
During the strategy stage, you considered how data is accessed and used among different departments. Often, departments work individually and with independent standards and procedures, working on responsibilities and goals that seemingly don’t apply to other parts of the organization.
Employees who are used to functioning within this limited capacity do a disservice to themselves and their organization—but the fault isn’t all their own. If your company operates with a competing vs. collaborative mindset, silos will continue to plague your organization’s development and limit vital decision-making and transformational initiatives.
No matter how much automation an organization applies, true insight must come from people. Equipping teams with the tools and knowledge to access, use, and—critically—protect company data enables them to break out of silos and leverage insights from multiple arenas to elevate the entire organization.
The lesson: equip teams with the data they need to chase down the big-picture goals. The data landscape is vast, but the power of an educated team is limitless.
Here’s where the fun of data enrichment starts: integrating data to augment existing services, create new ones, and become an industry pioneer.
Data integration acts as the meeting ground for data pulled from various sources. As more and more new data flows into your organization minute by minute, integration cuts down on the time needed to prepare and analyze data, and contributes to overall data continuity.
With a single, comprehensive view for consolidated data management, users can pull valuable insights regardless of type, structure, or size.
The benefits of integrated data include:
Using integration to bring real-world value became a reality for a telecom giant Launch partnered with. This company put their vast amount of untapped viewership data to use and built a new ad platform that made $2 billion in its first year—and was deployed in under 6 months. Their advertising data portal provides real-time insights for their customers so they can make intelligent decisions about advertising spend.
Integrated data is opportunity in data. It enhances operational processes, opens new doors, and puts an organization on the ultimate edge of innovation.
Steps 1-4 give a company what it needs to pioneer a data revolution, but turning employees into explorers is another thing altogether. That’s why the last step in the data roadmap is creating a data movement in your organization. That includes managing data and engaging people.
When it comes to managing data, organizations must contend with a range of regulations, security initiatives, skill gaps, and budget constraints. A handy companion to your data management strategy is business intelligence (BI). BI tools are used to make sense of the vast array of data flowing in and out of businesses. It is, in short, a digital aid to assist your teams in data-focused decision-making.
To ease your employees into a truly data-driven company culture, training is key. If your employees recognize the benefits of adopting data-centric practices for both themselves and the business, success is more attainable.
Earlier, we mentioned the competing vs. collaborative mindset often found in organizations. Imagine a marketing team conducts an in-depth survey of customer satisfaction. The results of the survey are mostly favorable, but the marketing team notices a high percentage of negative feedback based on a specific product function. The marketing team takes this data and uses it to craft the next iteration of surveys without sharing the results. In this example, the marketing team isn’t necessarily competing with other departments, but they are keeping their data in a silo.
Ideally, the marketing team would share the survey data with the product development team to address the negative feedback.
Getting your entire organization on board to become data-driven won’t happen overnight. But, when employees are given the tools they need to thrive in a digital-first landscape, early adoption is more likely, and success is imminent.
To review, the five steps for a triumphant data roadmap are:
But you can’t build a path forward without first understanding what step you’re on. Launch is proud to offer the Future State of Data Workshop, an interactive working session that envisions your next steps to transformation.
While orgs of all sizes and sectors can take this roadmap and start building a comprehensive data strategy, navigating with purpose to create that future state results in unique insights for your specific organization. Whether you’re on Step 1 or Step 5, crafting a plan to reach your destination will help you become a data hero for your business.
