For companies today, making data driven decisions is essential to gaining competitive advantages and guiding growth. The lifeblood of this “business intelligence” is access to quality, relevant data. That’s why Sterling Data Acquisition for Business has become a crucial strategic capability for enterprises large and small.
This article will examine why acquiring and aggregating data from both internal and external sources provides the cornerstone for actionable business intelligence. We’ll also look at leading techniques and technologies companies now leverage as part of a sound data acquisition strategy.
Importance of Data Acquisition
Data acquisition is the cornerstone of BI for several reasons. It forms the foundation upon which all subsequent BI activities are built. This process enables organizations to gain insights, make informed decisions and respond to market dynamics. Without reliable Sterling Data Acquisition for Business, BI would be like a ship without a compass.
The Evolution of Data Warehouses and Lakes
Traditional on-prem data warehouses have limitations in the breadth of data they can integrate and the speed at which they operate. Modern cloud-based data lakes built on storage innovations can house exponentially greater datasets and leverage technologies like Spark for faster processing.
When combined with populating ETL pipelines, data lakes take flexibility and scale of data acquisition to new levels. Companies must re-architect legacy warehouse capabilities for the data lake-driven world.
Emergence of the Chief Data Officer
Given rising strategic importance, more enterprises now pioneer a new C-suite role – the Chief Data Officer (CDO). CDOs not only oversee data acquisition, but also drive data governance, ensure data quality and champion a data-driven culture across the business.
Forward-looking companies understand dedicated leadership is required to harness the power of data at scale. The CDO orchestrates the people, processes and technologies to maximize the value of one of today’s most vital enterprise assets – its data.
Acquiring Data Responsibly
With data acquisition in overdrive, responsible data collection, use and monitoring grows paramount. Beyond mere regulatory requirements, companies must safeguard customer trust through transparent opt-in policies, access controls and ethical AI training protocols that root out bias in automated systems
By becoming conscientious stewards of people’s data, rather than simply exploitative consumers, modern enterprises can gain both a performance edge and moral high ground over competitors. After acquiring data, you must make sure to keep it safe from internet attacks. This is where our article on data-centric cybersecurity comes into play. Check it out and learn how to keep sensitive information well-protected and safe in today’s world.
Components of Effective Data Acquisition
Comprehensive sterling data acquisition for business involves:
Structured Internal Data
Pulling information from corporate databases, CRM and ERP systems, inventory and sales data, web analytics, etc. Provides visibility into core operations.
Unstructured Internal Data
Gathering human generated data like documents, emails, chats and social media content. Valuable for sentiment analysis.
Licensing third party data like credit reports, real estate records, demographics and more. Essential for customer and market insights.
Accessing and analyzing real time data from sensors, APIs, clickstreams and monitoring tools. Critical for responsive operations.
Increasingly, firms draw value from open data initiatives by governments, non-profits and communities to complement proprietary data.
The Data Acquisition Team
Given this complex technology landscape, many enterprises now convene cross-disciplinary data acquisition teams encompassing:
- Data engineers to build and manage data pipelines
- Data stewards to govern policies and standards
- Business analysts to identify critical information needs
- IT to provide infrastructure and tooling
- Executives to align efforts with strategic objectives
With the right governance and skill sets in place, these teams are best positioned to drive the comprehensive data acquisition process..
Leading Data Acquisition Technologies
Top platforms like SQL, NoSQL databases and data lakes provide the capabilities to extract, store and integrate data from this myriad of sources into unified architectures. Meanwhile, technologies like ETL (extract, transform, load) tools, data virtualization, change data capture and data replication smooth the complexity of moving data through these pipelines.
Machine learning techniques can also assist by structuring and mapping unstructured data sets into standardized schemas more suitable for analytics. As volume, variety and velocity of data explode, AI and ML grow increasingly vital for efficient, automated data acquisition capabilities.
Data Ops: Maximizing the Whole Pipeline
Getting data in is just the beginning. To optimize results, companies increasingly borrow from DevOps to implement Data Ops frameworks. Data Ops applies CI/CD and Agile principles across the extended analytics pipeline – from acquisition to activation.
With interconnected, automated flows between data ingestion, preparation, model development and deployment, Data Ops accelerates digital insights at cloud speed and scale.
Emerging External Data Marketplaces
A burgeoning ecosystem of data marketplaces has arisen to meet demand for third-party data acquisition. These include:
- Data brokerages aggregating licensed public and proprietary data
- Vertical SaaS data apps for niche domains
- Data exchanges facilitating transactions
- Cloud data catalog services indexing external data sets
By researching marketplace options, companies can gain access to incredibly enriched, targeted data assets to drive enhanced analytics. The right external data sets can infuse predictive models with unique signals and independent variables previously unavailable.
The Expanding Role of Data in Business
High quality data fuels everything from strategic planning to daily operations by Sterling Data Acquisition for Business:
- Segmenting customers
- Predicting sales
- Optimizing supply chains
- Personalizing marketing
- Tracking KPIs
But as the importance of data analytics has grown, so have the complexities of gathering all this data. Critical information exists both within a company’s walls and outside across an expanding array of sources. This diversity of data landscapes begins the rise of dedicated data acquisition.
Why a Sound Acquisition Strategy is Essential
Simply put, better inputs drive better outputs. Thoughtful data acquisition ensures you collect:
- The right data: Information that’s actually relevant to strategic and operational needs.
- Quality data: Accurate, timely data that leads to trustworthy insights.
- Integrated data: Consistent data from across silos aggregated into unified views.
Without these foundations, the business intelligence and analytics built atop the data lose significance and reliability. The axiom “garbage in, garbage out” rings true – no amount of analytics can compensate for incomplete, outdated or siloed data.
A great data acquisition strategy is no longer optional for business. By providing comprehensive, high quality data, acquisition forms the vital cornerstone for all downstream analytics and intelligence.
Companies must focus resources on sourcing, aggregating and governing data from across both internal and external ecosystems. With sterling data acquisition for business mastered, the insights uncovered and innovations fueled will drive better decision making and performance.