Make Your Data Work For You
Digital Transformation
Transform Your Work Processes
Digital transformation is more than another buzzword with a meaning that’s hard to pin down. Broadly, it refers to the process of incorporating digital technologies and data into every part of a business to improve performance, increase efficiency, and increase adaptability. Digital transformation is characterized by constant flux. Managing digital transformation requires a strategy flexible enough to meet unexpected changes.
Technology has fundamentally altered the way we do business: where technology used to support business functions, today it drives business and creates opportunities. Digital transformation finds openings to use technology to improve operational performance and workflows while also improving customer relations and user experience. It streamlines communication and automates tasks for repeatable results.
Digital transformation, in simple terms, means using digital tools to make your data work for you.
Why Do I Need Digital Transformation?
Digital transformation technologies enable organizations to collect, store, and analyze large amounts of data to gain insights that can help drive growth and improve performance. While many of the new tools available require large investment and planning to implement, tools like data dashboards are quick to design, test, and implement into your workflow. And the results can be seen almost immediately.
Stages of digital transformation:
Stage One: In the first stage, paper processes become digital. Paper forms, spreadsheets, and reports are digitized.
Stage Two: Once information is digitized, new ways of managing data are needed. Data storage and control features must be managed.
Stage Three: Data management first falls to individual departments to organize and automate processes. Digitization, data management, and process automation may be uneven across departments.
Stage Four: Enabled by improved digital tools, workflows and business processes need to change to reap the benefits of true digital transformation. Data verification, standardization, and governance are needed to build a resilient data culture using digital tools not as replicas of physical processes, but in ways that shift and transform all aspects of business.
Stage Five: The final stage of digital transformation is a never-ending process of incorporating digital technology to create value.
Business Intelligence for Smart Transformation
Using business intelligence tools such as intuitive data dashboards in combination with digital field data collection tools and new technologies, organizations can collect, store, and analyze all kinds of data, making decisions driven by data rather than instinct. Business intelligence tools can analyze data from a wide range of sources – financial, environmental or regulatory, asset management, and public relations – using this information to identify patterns and trends for decisions that are driven by what the data reveal. Widespread access to key data helps break down data silos for better analysis and decisions.
KJ’s Approach to Digital Transformation
KJ’s approach to digital transformation begins by working with stakeholders to understand the visions and goals of the organization. We assess the current state of digital transformation and perform a digital maturity assessment to pinpoint where using business intelligence and other digital tools and technology can have the most impact. We work to improve business processes by using existing data assets and technology-focused initiatives already in place at the organization. Working with clients to understand specific business goals, we develop a roadmap incorporating tools and processes for immediate results while also identifying tools like cloud computing, artificial intelligence, and the internet of things (IoT) that can provide new, real-time, and easy-to-access data. These tools allow organizations of any size to collect, store, and manage the data upon which business intelligence depends.
Digital transformation does not yield a fixed set of practices or static end state – it is a shift in mindset to find ways to strategically employ technology to drive business development and create value while also improving business resilience and agility. Different industries are in different phases of maturity in the digital transformation realm. And within a single organization, there can be differing levels of maturity, with some departments (financial, billing) being highly digitalized and others still working from spreadsheets or paper forms.
At KJ, we have found there is no one set approach for comprehensive digital transformation within an organization: each plan needs to consider existing data maturity, business goals, and appropriate tools to meet needs now and into the future. The essence of digital transformation is becoming a data-driven organization — ensuring that key decisions, actions, and processes make use of key data-driven insights — without losing the value of human intuition and experience.
Verify Your Data to Trust Your Results
A first step to digital transformation is developing data dashboards for improved communication and tracking. But dashboards can only be useful if the data they integrate is accurate. If your organization lacks consistent data standards or relies on paper forms for data collection, data verification and data cleansing may be essential steps. Verifying your data means confirming that your data is accurate and mostly error-free. Before using data in decision-making tools, its accuracy should be confirmed. Cleansing your data means reviewing source data for errors, irrelevancies, and inconsistencies, and finding ways to restructure data so that it can be read by a computer and easily drawn into a visualization tool.
Data verification and data cleansing are commonly part of data migration, where migrated data is checked against its source to confirm the transfer and identify errors. Even though dashboard development does not require data migration, this step can prevent duplicating data or metadata in ways that can interfere with dashboard development. Data verification and data cleansing are part of data quality assurance for data that is reliable and trusted. Errors identified during this process can guide changes in data governance and data standards.
Data Governance for Consistent Data Management
Data governance defines the strategy – the roles and rules – regulating your data. The goal is consistent, high-quality data that can be used for effective decision-making. The path is through collaborative governance tools: planning, monitoring, and enforcing rules for how to manage your data. Governance focuses on developing policies for getting your people to do the right thing with their data.
Data governance establishes key policies for data quality, availability, usability, integrity, and security. Data governance results in:
Better data: Helps keep data accurate, consistent, and high-quality
Safer data: Protects data from unauthorized access or use through data security policies
Leaner data: Improves data sharing and collaboration, and reduces data redundancy and storage costs
Reliable decision-making: Improves reliability and accuracy of data for confident decision-making
Compliance: Improves compliance with legal and regulatory requirements (such as privacy and use restrictions)
Data governance requires aligning your team – there must be clear ownership and accountability regarding your organization’s data. If your policies have lagged behind your adoption of technology, then your governance policies may encounter some resistance to change. The earlier you establish governance, the better your team will respond.
Encourage your team to embrace a “change” mindset, preparing them for a future of constant adaptation as technology shifts and improves.
If your organization is large, governance may be difficult to initiate. Segmented businesses with entrenched data silos may resist sharing access. While departments can generate their own data governance policies, effective data governance should be part of the organization. Data governance provides the terminology, performance indicators, rules, and policies that apply to your data. Once implemented, data governance means leaner, cleaner, more reliable data for better analytics and better decision-making.
From Data Governance to Data Standards
Data is one of your most important assets. Most businesses recognize this but struggle with historically siloed business units with data housed in multiple locations and in multiple formats. Data standardization is the process of formalizing data into a consistent format across an organization for better communication between departments and better data integrity.
Data standardization can be simple: establishing naming conventions for files or using consistent units of measure can quickly improve data quality. Standardized data collection is faster and more accurate. Standardizing data format makes it easier to search for key data points and compare across files. Consistent organization of data components and metadata makes it easier to locate files and draw them into business intelligence visualizations. With metadata standards, there is less likelihood of redundant data cluttering your files.
Establishing data standards can involve exploring the data your organization currently captures and standardizing data collection and storage practices for easy retrieval. The process of standardizing data can uncover additional issues with data capture that need fixing. For example, when standardizing a well sampling form for consistent data collection, you may realize that a digital form with preselected fields provides greater accuracy and consistency, yielding more standardized data.
Once a digital solution is selected, a storage location and naming convention should be selected. Rather than simply replicating the paper form, consider what additional information might be captured for other uses, such as automatically tagging the GIS location or a photo of current conditions. Data standards can improve your access to the data you are already collecting and make it more widely available to your organization for business insights and shared potential.
Data Revolution Requires Data Culture
The data revolution is here and there is no looking back.
To jumpstart your own data revolution, your workplace culture needs to become a data culture — a culture that values data and data-driven decision-making. The global shift toward digital transformation is uneven, and there is a growing gap between those who have implemented the digital technologies and processes that support data culture and those that have not.
Culture cannot be changed by decree, but it can be influenced. Large organizations may struggle to implement changes that drive culture across many departments, sites, or even countries. If you want to support data culture at your organization, consider making data a central part of the conversation. Identify your key performance indicators tied to culture and share them with your team. Make culture a central part of your company-wide conversations. Let your teams know how data will improve business processes and performance, and how those improvements will affect your teams.
By making information more accessible to your teams, you can give them the tools they need to make the decisions they already make, but better informed and more grounded. Give them tools like business intelligence and data dashboards to streamline their internal processes, and you will quickly find that data culture develops a life of its own. Once people get a taste of what streamlined, accessible data can do, they will be hungry for more.
And once your team has an appetite for data, they can begin innovating and improving their workflows based on the data they have.
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