Data science has quickly become one of the most in-demand skills globally. As a result, even sophisticated enterprises often seek outside help unlocking value from data. This guide provides a comprehensive overview of the data science consulting ecosystem to help you successfully navigate engagements.
Defining Data Science Consulting: Key Capabilities
Data science consulting encapsulates advisory and implementation services around extracting predictive insights from data. Consultancies provide strategic guidance as well as technical delivery spanning:
Data Strategy Development
- Identifying business issues amenable to statistical / machine learning approaches
- Audit data sources, surface highest potential use cases
- Account for relevant regulations, data security considerations
- Craft detailed project plans and product requirements
Strategy development requires synthesizing perspectives across functions:
For example, a manufacturing firm could leverage sensor data analytics to optimize energy consumption across sites. This would require coordination with on-site engineers to configure data collection as well as executives to align on infrastructure upgrade budgets.
Whereas an e-commerce firm interested in maximizing sales through improved recommender systems would need to work closely with marketingteams to align on customer segmentation and legal to ensure data practices comply with consumer privacy laws.
Each business vertical has unique challenges and objectives that drive data strategies:
| Industry | Common Data Science Use Cases | Key Considerations |
|---|---|---|
| Manufacturing | Predictive maintenance, inventory optimization | Data quality, platform scalability |
| Telecom | Churn reduction, cross-sell / up-sell | Data integration complexity, regulatory constraints |
| Banking | Fraud detection, credit risk modeling | Interpretability, ethical model use |
| Healthcare | Patient health predictions, clinical trial analytics | Strict privacy standards, specialized data types |
| Retail | Recommendation engines, promotion targeting | Capturing unstructured data, speed of insight |
Data Science Solution Validation
Before fully greenlighting project investments, assumed impact and feasibility should be pressure-tested:
- Clarify expected business value through financial modeling or minimum supported assumptions
- Examine barriers to adoption like culture misalignment or change resistance
- Propose tests to confirm data quality and model accuracy on sample data
- Outline mitigation plans if results underperform expectations
Separating initial strategy from validation enhances objectivity and incrementally reduces risk.
Model Development & Technical Delivery
Turning strategies into solutions requires hands-on analytical and engineering work:
- Ingesting and exploring datasets with statistics and visualization
- Feature engineering based on domain expertise
- Applying ML algorithms like regression, random forests, neural networks per use case specificity
- Translating models into applications through Python, R, Spark, SQL, etc.
- Validating accuracy on test datasets and target metrics
- Ensuring technical scalability via cloud or optimized on-premise environments
The exact combination of tools and techniques depends highly on the problem complexity, available data, and performance goals.
Training & Knowledge Transfer
Even with excellent solutions delivered, long term success requires buy-in across stakeholders. Consultants must invest heavily in:
- Educating teams on incorporating analytical insights into decisions
- Documenting methodologies for oversight and reproducibility
- Identifying power users to support continuous enhancement initiatives
- Standing up governance procedures for model monitoring and retirement
With improved fluency around how analytics improves processes, internal adoption results in sustained competitive differentiation.
In summary, competent data science consultants not only advise on strategy, but also deliver and disseminate solutions tailored to clients‘ needs. This encompasses both technological capabilities as well as change management acumen.
The Data Science Industry Continues Its High-Velocity Evolution
The data science consulting ecosystem now spans thousands of firms globally across technology vendors, boutiques, large multi-nationals, and in-house capabilities.
MBB giants like McKinsey, BCG and Bain now compete with traditional SIs and specialty analytics shops to capitalize on surging enterprise AI demand. Small teams of elite data scientists can provide nimble alternatives as well.
And Increasingly, internal build approaches supplement external partnerships.
Here are some interesting trends as the competitive landscape develops:
MBB Firms Rapidly Building Technical Chops
MBB strategists are renowned for rigorously evaluating addressable markets and delivering immense business impact. Examples range from Bain‘s due diligence supporting Microsoft‘s LinkedIn acquisition to McKinsey‘s cost optimization empowering mobile providers to extend 5G access.
Yet as more analytical opportunities demand deep technical expertise alongside business strategy, we‘ve seen aggressive investments in analytics acceleration from consulting titans.
BCG touts over 2000 data scientists and engineers now actively modernizing clients through AI transformation.
Buoying talent with acquisitions as well – like McKinsey bringing onboard more than 60 data engineers by purchasing PUNQ in 2022.
Still, MBBs often excel more with blueprints versus detailed implementation. Integrating and retaining hardcore engineers poses cultural challenges as well, evidenced by some turnover among hires.
So large tech consultancies continue commanding the lion‘s share of sheer project volume given engineers at their core.
Big Tech Racing to Empower Data-Driven Decisions
Global service integrators like Accenture, Deloitte, Capgemini, TCS and Infosys field armies of certified cloud and analytics experts sought by enterprises undergoing large-scale digital overhauls. While smaller specialists focusing exclusively on analytics like Mu Sigma, Fractal and LatentView boast capabilities rivaling the majors.
These vendors emphasize hands-on platform expertise with leading analytics toolsets from Databricks, Snowflake, DataRobot, etc. And they‘ve retained their distinction delivering raw computing horsepower to crunch big data into game changing insights.
GSI strengths also include well documented methodologies around bridging on-premise, hybrid and multi-cloud environments – key with so much sensitive data still sitting behind firewalls unable to move.
Yet smaller competitors counter with extreme project focus in analytics niches allowing tighter client collaboration and innovation.
Boutiques Offer Nimble Specialization
Boutiques firms sporting elite data scientistsdeliver advantages through their concentration – often combining deep vertical expertise with excellence mastering key techniques like NLP, predictive modeling for IoT data, anomaly detection algorithms, etc. that perfectly align to client interests.
For example, Opera Solutions touts a long track record optimizing supply chains by predicting equipment failures from sensor feeds. Whereas ACE AI Consultants developed a proprietary enterprise search solution utilizing transformers and other advances in NLP.
These groups boast success winning marquee Fortune 500 relationships when the problem tightly maps to resident PhD-level skills.
Compared to massive onboarding programs at giant shops, boutiques promote partner-level attention and knowledge transfer working alongside internal analytics teams to target complex modeling challenges. Limits on scale can constrain bandwidth however.
So ultimately, enterprises access world-class support through a spectrum of consulting partners catering to needs ranging from targeted technical firepower all the way to fully outsourced development.
Making Smart Consultant Selections
With so many capable analytics consultancies in play, UC Berkeley suggests focusing primarily on 3 factors when deciding on partners:
1. Relevant Background & Specialization
Table stakes are demonstrated competency apply data science to challenges directly related to the client‘s industry and use case.
Long track records over years codifying industry quirks into highly accurate models is ideal – implying no shortcuts when assessing vendors. This spurs creative ideas grounded in reality.
Firms sporting published thought capital around innovations your vertical can prove interesting prospects as well. Such content essentially provides free RFI/RFP support.
Besides domain expertise, clearly understand each contender‘s technical capabilities – whether core strength in statistics, machine learning, NLP, etc. These should appropriately fit the problem with reasonable success indicators to expect.
Some scenarios better suit different consultants based on their stacks:
| Client Objective | Preferred Consultant Focus |
|---|---|
| Analyzing molecular interactions to accelerate drug discovery | Bayesian deep learning, biological data familiarity |
| Parsing contracts to automate legal processes | Text analytics & NLP, legal document experience |
Avoid mismatching ambitions to advisor abilities, which sinks budgets into generalized troubleshooting versus progress.
2. Change Management Skills
The best consultants bridge advisory strategy with smooth adoption and sustainability of prescribed actions. This means not just technically proficient advisors but true translators who interpret analytical insights into business outcomes.
Soft skills accordingly rate highly when engaging advisors:
- Communication: Explaining technical concepts through relatable analogies
- Emotional intelligence: Encouraging collaboration despite internal politics
- Creativity: Framing unconventional applications of data science for competitive advantage
- Storytelling: Distilling complex analysis into compelling cases for leadership endorsement
Since data insights must channel into processes owned by business units, seek consultants invested in transferring ownership – through governance strategies, training programs, pairing arrangements, etc.
Ideally, partners mix technical qualifications with change management experience to promote sticking power for advanced analytics.
3. Commitment to Continuous Education & Improvement
True thought leadership requires keeping pace with bleeding edge R&D before those innovations manifest in public tools and platforms.
Look for consultants actively:
- Publishing award-winning research dissertations in academic journals
- Committing hours to continued STEM/analytics skill development
- Holding prominent industry conference speaking engagements
- Leading open-source technology initiatives on GitHub
Testing around understanding of latent semantic analysis, Bayesian causal networks, reinforcement learning applications, etc. uncovers genuine commitment to expanding frontiers over simple proficiency applying current technology during engagements.
As alternative data transforms analytics, synthetic data creation techniques will similarly appreciate greatly. So thoughtfulness around emerging data science trends serves as a proxy for aligning long-term engagements rather than one-off surges.
Noteworthy Data Science Consultancies
Many exceptional firms could suit partnering needs. Here is a global cross-section across boutiques, big tech consultancies and MBB firms:
| Consultancy | Headquarters | Size | Strengths | Sample Clients |
|---|---|---|---|---|
| BCG Gamma | Global | 1000+ consultants | Strategy defining analytics capabilities assessing value. | Johnson & Johnson, Lufthansa, Doconomy |
| QuantumBlack | London, UK | 250+ consultants | Specialists providing AI/analytics platform for process improvement & data-driven decisions. | Shell, Starbucks, UK National Health Service |
| LatentView | Princeton, NJ | 1500+ consultants | Deep analytics firepower through full lifecycle of insight generation and operationalization. | Top 5 global pharma company, multinational CPG leaders |
| Fractal Analytics | Mumbai, India | 2500+ consultants | Customer centricity focus via AI/ML, IoT, computer vision and other emerging techniques. | Global bank, Fortune 50 retailer, agriculture industry giant |
| Slalom | Seattle, WA | 10,000+ consultants | Emphasis on building analytics tools and capabilities rather than one-off optimization. | General Mills, AT&T, State of Washington |
| CENTRIC Consulting | Boston, MA | 350+ consultants | Specialized model development using optimization, segmentation, forecasting and machine learning. | Aramark, BioMerieux, Brinks Home Security |
| Elder Research | Virginia, US | 100+ consultant | Custom modeling and development of analytical apps spurred by data science innovations. | US Navy, International sportswear firm, Medical center |
| MDH Analytics | Munich, Germany | 40+ consultants | High impact computer vision solutions targeting quality assurance and resource efficiency issues. | Automotive, electronics, packaging multinationals |
| Lexalytics | Boston, MA | 75+ consultants | Optimizing business decisions through ML-enabled voice of customer analysis. | Schneider Electric, MathWorks, TimeTrade |
Key Takeaways & Next Steps
In closing, choosing data science solution partners requires scrutinizing:
- Proven expertise maximizing business value from data amid industry constraints
- Combination of analytical talent and change management skills to enable adoption
- Commitment to continuous education fueling creative, future-proof recommendations
With so many specialties and strengths across vendors, thoughtfully evaluating consultant competencies, communication modes and vision pays dividends determining fit.
Weigh the types of skills (big picture strategists, nimble coders, etc) against use case urgency and scope.
Readily accessible deliverables also promote smooth hand-offs communicating tangible impact, enhanced workflows and improved performance to leadership. This sustains support for additional initiatives and capabilities advancing analytics maturity over time.
For hands-on assistance identifying partners fitting your needs and ambitions, please visit AIMultiple.com to connect with providers directly or get personalized recommendations.
Our network of deeply technical MBB, boutique, and big 5 data scientists stand ready to explore achieve breakthroughs with your teams.
Reach out and let‘s get started accelerate leveraging your data‘s potential now.