In a digital economy gathering and analyzing data is key to unlocking the mysteries of the questions every team faces; Where can we improve? What’s working? What’s not?
The data itself serves as a light in the dark revealing insights into how an organization actually operates day-to-day. This enables a shift from being reactive to proactive.
The complexity of data systems grows every day. Many companies have specific software for each function from Payroll, Accounting, HR, ERP, Billing, CRM, analytics, and industry-unique systems.
Data and technology stacks house growing amounts of fast-changing data, scattered across the organization, often with no standard structure and external sources of great value, not considered or captured at all.
Organizations are making some of their largest decisions based on limited data and are starving for useful insights.
A Digital Ecosystem with Transparent Data
Establishing a comprehensive 360º trustworthy view of data sets the foundation for analytics.
The first step needed to achieve data-enabled decision making across an entire organization is to bring together current available industry systems and data under one roof. It is critical for every part of an organization to share necessary data to prevent data silos.
Gathering and unifying relevant data to deliver a single source of truth empowers the workforce with a consolidated view that saves time and elevates productivity.
One digital ecosystem to host a digitized company-wide data version enabling powerful analytics to outperform.
Machines can look at lots of high dimensions of data and determine patterns. If the machine can learn this pattern, it can discover anomalies, spot trends, and generate predictions based on learning. Once patterns are learned predictions can be made that humans can’t even come close to.
This is machine learning and part of the world of AI in which data scientists extract insights from data to solve complex challenges. The right data enables all manner of actionable insights across all business activities for quantifiable top and bottom-line impact.
Collaborating with stakeholders, data scientists:
- Design and test new ways to generate data
- Sift and prepare existing data
- Conduct exploratory data analysis, Al- Machine learning concept model identification, pattern and anomaly detection, and predictive model creation
- Identify and validate use cases for decision making
- Develop and validate process or business models
- Develop integration strategies into the operational business
System of Insights
Each data-driven business decision-making problem is unique; comprising a combination of goals, desires, and constraints.
There are sets of common tasks that underlie a business problem. Recognizing familiar problems and solutions avoids wasting time, enabling greater attention on parts requiring expert involvement where human creativity and intelligence are paramount.
For this reason, we categorize insights across business activities such as; Risk, Services – incorporating operations, logistics, and procurement – Sales, Marketing, Talent, and Data discovery insights generally. Data science enables a companywide system of insights across all business activities.
Companies need to accelerate a move towards digitization and consider how to leverage new technologies and data science expertise to allow for a faster flow of data.
Broad companywide insights
In a digital economy, the traditional interrelated value chain activities are fluid and evolving at speed.
The ability to generate real-time insights draw the intersection of roles and function ever closer, blurring and redefining responsibilities and accountabilities across the entire value chain.
Most people are at least vaguely aware of the evolution of agile digital marketing;
“Using data and analytics to continuously source promising opportunities or solutions to problems in real-time, deploying tests quickly, evaluating results, and rapidly iterating.”
Insights from data help marketers understand how to attract new customers and delight existing clients. Data scientists help by classifying past and present data, detecting patterns, anomalies, and trends, and identifying relevant models to predict the future. Insights across interrelated value chain activities and functions of marketing, sales, and service help organizations identify demand, develop and create new services, products, and experiences. Revenue teams are primed with intelligence on market trends, shifting customer demand, pricing appetite, discovering what customers want now and what they will want in the future, and with predictive modeling even which customers are most likely to leave and why.
(RevOps) is an example of the convergence of interrelated roles made possible by the speed and volume of data production, and the ability to ingest and drive insightful business intelligence.
Revenue operations are the strategic integration of revenue-related roles, sales, marketing, and service departments. The main goal of RevOps is to connect data from these departments to provide a better 360º customer view before, during, and after the sale. As digital customer experience evolves, the need for various departments to share information has grown. RevOps is the strategy to bring them all together to precisely manage and measure the return on investment (ROI). It takes responsibility for the software, systems, processes, and data for all the revenue-generating teams inside a company.
Valuable outcomes from previously unseen insights are waiting to be discovered across the entire value chain, primary and support activities. Derived from each independent activity and activities collectively such as RebvOps or supply chain and the interrelated activities of procurement, internal and external logistics operations, and human resources.
Typically when planning, purchasing, and production departments work in silos, information gets delayed or goes un-communicated, slowing down the process and resulting in part or completely uninformed decision making. Synchronizing data provides opportunities for transparency and collaboration. Shared data leads to faster response times and a more efficient process overall.
Departments can have more frequent and real-time access to information across all channels, allowing for better evaluations and informed decision-making. Such data synchronization reduces redundancies, improves communication, and offers a clearer insight into channels. It also gives companies the advantage of better predicting demand and identifying risks, making forecasting and risk assessments more accurate and helping to meet demand with efficient effective supply.
Regardless of business function from Sales, Marketing, Operations, Services, to Human Resources and Risk management sharing a single source of trustworthy data empowers an organization to derive symbiotic actionable business intelligence for a competitive edge.
Designing systems of insight
A data strategy steers a data stack underpinning technology stack, systems, and overall enterprise architecture enabling data scientists to collaborate with stakeholders and develop systems of insight for a competitive advantage.
A critical skill for a data scientist is the ability to decompose a data analytics problem into pieces that match a known task for which tools are available. The Data Science and AI landscape provide a deeper dive into the evolving, growing battle for resources.
Together let’s find your use cases and start to build your systems of insight.
Turn data into actionable insights
Implementing data science is part of a digital transformation journey taken in stages, applying a simple 3-step methodology, gathering, analyze and acting to drive organizational data-enabled decision making. Alephnet’s services help you develop your own insights ecosystem that delivers real-time actionable insights across all business activities regardless of your industry.