Let‘s start with the basics – what are data repositories and why should you care?
Data repositories refer to centralized systems that store, organize and manage data for reporting and analytics. They empower you to tap into the gold mine hiding within all the data your business captures.
With the right repositories, you can transform scattered, messy data into valuable business insights. Think of it as turning coal into diamonds!
Intriguing isn‘t it? Read on as we uncover everything you need to know about data repositories.
Why You Need Data Repositories
Imagine an old library where books lie scattered all over the place. Some shelves are labeled History or Science but several books overlap across sections. Others lie in unshelved piles spread across tables and the floor.
Finding the exact book you want is tedious. And often you piece content from multiple books to get the full picture.
Now picture a modern library with an online catalog, clearly labeled sections and librarians to help out. Simply search to find the exact book you need. This neatly organized system makes discovering information easier.
Data repositories offer this same value. They consolidate scattered data into a structured, centralized warehouse – ready for analysis.
Let‘s check the key benefits:
Single Source of Truth
CIO surveys reveal that poor data quality leads to around $15 million in losses annually across enterprises. Shocking, right?
This bad data hides in fragmented application silos. Marketing platforms show 10k customers while billing systems have invoices for 15k users. So who‘s right?
Data repositories fix such chaos by merging data from different systems into a single source of truth. The result? No more finger-pointing between departments working off different numbers!
How much can you save with consistent enterprise data? Over $700k in most organizations according to Data Warehousing Institute.
Deeper Data Analysis
Trying to analyze customer behavior across your website, app, support emails and chats is no easy task when data resides in disconnected systems. Instead you get fractured analysis.
Data repositories solve this by structuring and linking related data elements across sources. With a unified view, analysts uncover deeper revenue and experience insights.
For instance, web visits and support ticket data may reveal an lump in complaints after a website change. Address issues faster through integrated analysis vs siloed efforts.
According to Forrester, 97% of firms see improved analytics from consolidated data platforms. Who doesn‘t want sharper insights, right?
Improved Data Security
With data scattered everywhere, it becomes harder to keep it secure. When under pressure to make quick fixes, I‘ve seen admins provision unauthorized access violating compliance policies. Ouch!
Centralized repositories help take control through unified policies, access controls, audits and data masking. Managing permissions is easier via a single pane of glass vs multiple systems.
Mckinsey highlights how consolidated data platforms reduce breach risks by up to 73% while cutting compliance overheads. Talk about hitting two birds with one stone!
Sold on getting data repositories yet? Let‘s explore popular options…
Types of Data Repositories
1. Data Warehouses
The data warehouse remains the most common consolidated repository. Here structured data from transactional systems gets aggregated, cleansed, transformed and loaded into a unified database.
Feeds may include CRM contacts, purchase orders, inventory listings, loyalty schemes, web traffic etc. The unified store powers cross-functional reporting across sales, marketing, finance etc.
2. Data Marts
Rather than moving all data into a mammoth warehouse, data marts provide subsets of the warehouse focused on specific business units.
For instance, daily regional sales data relevant for area managers may go into a separate data mart. Data science teams get their own mart for experimentation without affecting business reports.
3. Data Lakes
For unstructured data like images, videos, social conversations and sensor data, data warehouses fall short. Instead, data lakes come to the rescue.
Think of data lakes as giant pools of raw, unfiltered data stored efficiently for deferred analysis. You provision adhoc access to users when needed vs frontloading schemas.
4. Hybrid Models
Leading organizations often leverage the best of both worlds. Data lakes ingest and archive raw feeds in cheap storage. Relevant data gets filtered into a warehouse for business reporting.
Specialized marts may further serve departmental needs. SoOPS teams monitor support ticket trends while product uses customer sentiment data from surveys.
The right architecture depends on your use case and infrastructure maturity. Onwards!
Designing Your Data Repository
Now comes the fun but critical part – actually planning and building your data repository.
Based on hands-on experience architecting customer data platforms, here are my 8 top tips:
1. Define Your Roadmap
What insights do you want from your data? Better personalization? Fraud prediction? Supply chain optimization? Smart maintenance?
Define the end goals and work backwards to the datasets needed. Balance completeness with what‘s feasible fast. Focusing on an initial high-value analysis area allows quick returns.
For example, a logistics firm prioritized route optimization as their first data repository use case. This provided ROI for expansion into predictive maintenance next.
2. Know Your Data
What data exists across your IT landscape today? List systems, access APIs, formats, data models and semantics.
This inventory helps assess fitment into your repository. Identify high priority datasets aligned to the defined roadmap.
Our logistics client mapped 140+ systems first. 14 core systems spanning routes, consignments and truck telemetry data were selected as initial inputs.
3. Clean Your Data
What processes exist for data quality, security and governance today? Factor them for your repository.
For instance, GDPR impacts European citizen data collection, retention policies and subject consent. These need compliance considerations even post consolidation.
Understand pain points and gaps too. Are admins provisioning access without audits? Is test data mixing with production? Bridge them through centralized controls.
4. Standardize & Model
Defining data standards upfront is crucial – it prevents painful rework down the road as systems scale.
Come up with standard schemas, meta models, dictionaries, security protocols etc. and enforce them centrally early on. Don‘t cut corners here!
Our client defined 230+ data entities with access roles and query patterns even before their first ETL integration was built! Talk about doing your homework.
5. Right Size Your Infrastructure
Estimate consolidated data volumes, query patterns and service levels needed. Factor in growth – storage and instances can scale as data increases over time.
Align technology decisions to this like cloud vs on-prem hosting, distributed clusters to prevent bottlenecks. choking points. Benchmark tool performance.
TIP: Consider serverless platforms over buying hardware. Services like AWS Redshift and Snowflake cloud data warehouses offer greater flexibility, scale and reliability.
6. Prioritize pipelines over repositories
Don‘t get hung up modeling the full data repository endstate at first. Focus on building resilient ETL pipelines from source systems incrementally.
These pipelines transform and shift data through cleansing, deduplication etc. before loading it into repositories. Perfect them first vs getting overwhelmed.
Our customer prioritized 24/7 ETL flows from their highest priority systems initially. The repository models and reports came later. Baby steps!
7. Automate Everything!
Once ETL pipelines reach maturity, invest in automation for performance and reliability.
Script testing routines to catch data issues early. Autoscale resources on demand. Containerize jobs for portability. Use CI/CD for version control and reproducible deployments.
Tools like Airflow, dbt or Prefect can orchestrate flows across cloud platforms like expert maestros!
8. Enable Access & Sharing
Democratizing insights should be the end goal. Structure access controls early on based on user roles and needs.
Engineers may need raw telemetry feeds while business teams prefer aggregated KPI dashboards. Make data discoverability easy through metadata catalogs as well.
Modern platforms provide built-in collaboration capabilities for users to share insights through self-service access controls.
Now over to you – follow these guidelines and craft the perfect data repository for your needs!
Implementation Examples
Let‘s see how leading enterprises have successfully leveraged data repositories:
LinkedIn‘s Data Pipeline
Professional social network LinkedIn handles 56 billion+ data changes daily – member profiles updates, new connections, content sharing etc.
To enable real-time analytics at this scale, LinkedIn built a robust data consolidation pipeline spanning:
- Databus – An open source change data capture layer pulling incremental data changes from online systems
- Espresso – A centralized data warehouse aggregating databus event streams for analysis
- Iris – An automated data orchestration system managing ETL pipelines round the clock
Together, this helps LinkedIn data scientists glean instant insights to improve member engagement.
Walmart‘s Data Cafe
Walmart consolidated over 2.5 petabytes of retail data from its stores into a curated ‘Data Cafe‘ data repository encompassing:
- Point of Sale Transactions
- Inventory Data
- Supply Chain Feeds
- External Data – Weather, Local Events etc.
Suppliers and store managers simply access Data Cafe for reporting and analytics to improve demand planning, logistics and performance.
So don‘t let data overwhelm you. Follow these best practices and let your data repository serve business insights on a platter just like Walmart!
Let Your Repository Chart the Course Ahead
That completes your crash course on planning, building and managing data repositories successfully!
The byproducts? A single source of truth across your systems. Trusted information for business decisions. Unified analytics and tracking.
While the problem appears gigantic at first, perseverance pays off. As the Chinese proverb goes:
"A journey of a thousand miles begins with a single step"
So compose your data roadmap, prioritize quick wins and start small. Double down on what delivers value. Before you know it, you‘ll be a data-driven organization!
Now over to you – build that repository, crunch some data and let me know how it goes!
